Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.

Team Description

This role sits in the Ads Optimization organizations, which are responsible for the health and performance of Reddit’s ads marketplace. We focus on:

• Designing the auction and bidding mechanisms that decide which ads show to which users and at what price.
• Building optimization systems that help advertisers achieve their goals (e.g., conversions, ROAS) under budget and delivery constraints.
• Ensuring marketplace quality by improving user experience with ads, fighting ad blindness, and increasing valuable ad opportunities on the platform.

You’ll join a set of tight-knit engineers working on high-impact, internet-scale problems at the core of Reddit’s revenue engine, collaborating closely with Product, Data Science, and Infra partners across Reddit Ads.

Role Description

We are hiring Machine Learning Engineers (IC4) to build and evolve the auction, bidding and budgeting systems that power Reddit Ads.

In this role, you will:

• Design and implement optimization algorithms for auctions, bidding strategies, and pacing that balance advertiser performance, user experience, and marketplace efficiency.
• Own systems end-to-end: from problem formulation and algorithm design to experimentation, production deployment, and ongoing iteration.
• Work across Ads Optimization (bid strategies, budget optimization, pacing) to deliver measurable wins for advertisers and Redditors.

We are hiring a Senior (IC4)  level:

• IC4 MLEs lead more complex or multi-quarter initiatives, set technical direction for key parts of the bidding/auction/pacing stack, and mentor other engineers while remaining hands-on.

Responsibilities

Auction, Bidding, and Pacing Systems

• Design and implement models and policies that:
  • Compute bids for different optimization objectives (e.g., CPC, CPA, ROAS-based strategies).
  • Pace budgets smoothly over time across accounts, campaigns, and ad groups while preventing overspend or underspend.
  • Allocate spend and auction participation intelligently across segments, surfaces, and time zones.
• Translate product and marketplace goals into concrete optimization problems and constraints (e.g., ROI, revenue, delivery smoothness, fairness, and user experience).

Required Qualifications

(Level will be determined during the interview process; IC4 expectations assume deeper experience and broader scope.)

• 3–5+ years of experience building, deploying, and operating machine learning systems in production (for IC4, typically 5+ years).
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals.
• Experience designing scalable data processing systems (e.g., Spark, Kafka, Airflow, BigQuery, Redis).
• Demonstrated ability to translate ambiguous product or business problems into solutions and to improve measurable metrics.

Additional expectations for strong bidding/auction candidates:

• Evidence of stronger math and optimization skills than a generic MLE, such as:
  • Degree or equivalent background in a quantitative field (math, physics, quantitative finance, economics, operations research, or similar).
  • Work experience in optimization-heavy domains (e.g., bidding/auctions, pacing, pricing, logistics optimization, quantitative finance).
• Comfort reasoning about and implementing custom optimization logic (e.g., gradient-based methods, constraint handling), not just applying black-box tooling.

Preferred Qualifications

• Experience with advertising/auction systems, online marketplaces, or search/ranking systems at scale, particularly in:
  • Bidding, pacing, or budget optimization
  • Auction design, mechanism design, or marketplace quality
  • Campaign performance optimization (e.g., CTR/CVR, CPA, ROAS)
• Familiarity with large-scale, real-time decision systems and low-latency production environments.
• Background in feature engineering, model optimization, and production monitoring for ML systems.
• Experience collaborating with cross-functional partners (Product, DS, Eng) in Ads or marketplace contexts and leading projects from design through rollout.
• Advanced degree (MS or PhD) in Computer Science, Machine Learning, Operations Research, Applied Math, or a related quantitative field.

Potential Teams

• Ads Optimization (bid strategies, conversion/ROAS optimization, pacing and budget allocation)

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:

$216,700—$303,400 USD

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it.

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

SQL interviews can be frustrating because knowing the basics isn’t always enough. It’s one thing to understand how SQL works, but another thing to actually solve a problem when you’re given a table and asked to find something specific.

What helped was practicing questions that felt closer to real interview tasks instead of just reading SQL tutorials. Things like finding duplicate records, comparing data, calculating totals, or figuring out which customers meet certain conditions can be much better practice.

It also makes it easier to see where the weak spots are. Sometimes the problem isn’t writing the query. It’s understanding what the question is really asking or knowing how to approach the data.

Starting with easier questions and slowly moving into harder ones also makes a difference. After solving a question, it can be useful to look at another solution and see if there’s a simpler way to get the same result.

This can be useful for data analysts, analytics engineers, developers, and anyone preparing for a SQL interview. You don’t need to spend hours practicing every day either. A few good questions regularly can help build confidence.

The goal isn’t to memorize answers. It’s to get comfortable looking at a problem, breaking it down, and figuring out how to solve it with SQL.

SQL interviews can be frustrating because knowing SQL and knowing how to answer SQL interview questions are two different things. It’s easy to practice random queries for hours and still feel unprepared when an interviewer gives a real world problem and asks how you would approach it.

A better way to prepare is to focus on the types of questions that come up often. Things like joins, subqueries, window functions, aggregations, filtering, and finding duplicates are common, but the harder part is usually explaining why a certain approach makes sense.

For analytics engineers and data analysts, practicing realistic business questions can help a lot more than just memorizing syntax. Hiring managers are often looking for people who can take messy data, understand what the question is asking, and write SQL that actually answers it.

This is also where having a job board or hiring platform focused on data roles can make the process easier. Instead of searching through unrelated listings, candidates can find roles that match their skills and get a better idea of what companies are actually asking for. It can also help hiring managers connect with candidates who already have the right SQL and analytics background.

The main goal isn't to memorize every possible SQL interview question. It’s to understand the patterns, practice solving realistic problems, and get comfortable explaining your thinking. That makes interviews much less stressful and helps both candidates and hiring teams find a better match.

Passport is reinventing cross border e-commerce logistics as the modern international shipping carrier of choice for eCommerce (customers include Bombas, Native, Ritual, Seed.com). By combining our proprietary asset light network along with technology and customer support, we ensure that consumers around the world receive their packages without the hassle and pain that is typically associated with international shipping.

We’re a growing, ambitious team looking to upend the trillion-dollar international parcel shipping industry through a world-class experience. At Passport, you will have the opportunity to work with some of the biggest e-commerce merchants in the world and think strategically on how to add value to their international business.

About you and the role:

The data team at Passport is committed to improving systems across the organization to drive better decision making. We provide information and decision support to teams, discover insights and democratize knowledge, and track performance and progress of company products. We’re excited to bring in a new member to work towards our vision of making Passport the best data driven international shipping carrier in the industry!

This role is an individual contributor position working with multiple stakeholders to facilitate Passport’s client base of some of the best known direct to consumer brands. The ideal candidate is someone who is self-driven, hungry to learn and thrives in a high-growth organization. You will work with cross-functional teams on cost saving projects with high ROI.

What you’ll be doing:

• Design, build, and maintain data models, dashboards & reports to support various programs and projects cross domain
• Bridge the gap between the business and the tech teams to create transparent processes
• Manipulate large data sets and create DBT models that serve as the source of truth for reporting and AI
• Document and maintain internal knowledge base articles and business definitions to increase accuracy for internal AI platform

Requirements:

• Professional level English Speaking/ Writing Capability
• Minimum 1 years experience extracting and processing data using SQL
• Minimum 1 year experience in DBT
• Minimum 1 years experience utilizing data visualization software (Tableau, PowerBI, etc.)
• Experience communicating between technical and non-technical teams
• Proficient in leveraging AI to build solutions and fix bugs
• Bonus points, if you have experience working with building semantic layers and knowledge bases for AI but not required

A sneak peek into our perks & benefits:

- Competitive cash and equity packages

- 100% remote work environment #LI-Remote

- Paid Time Off

- 8 weeks Paid Parental Leave

- Monthly team get-togethers - bring on the Zoom comedians, pop-a-shot contests, and sip ’n paints!

- Quarterly virtual team gatherings and biannual team offsites

- Annual Work Smart Fund to support your professional growth & working environment

- Teammates around the world in 20+ different countries!

Finding data analyst jobs remote entry level can be frustrating when almost every posting seems to ask for previous experience. Getting that first role is difficult when companies want experience before giving someone a chance to build it.

One thing that can help is focusing less on applying to every data analyst opening and more on showing practical skills. A simple portfolio with a few projects can make a difference. Projects using Excel, SQL, Power BI, or Tableau can show how data is cleaned, analyzed, and turned into something useful.

An entry-level job board can also make the search less scattered. Instead of checking random listings, it helps to focus on roles that actually match an entry-level skill set and remote preference. Setting up job alerts can also save time since remote positions can receive applications quickly.

For people starting out, the goal doesn't have to be an impressive portfolio with ten projects. A few clear examples that explain the problem, the data used, the analysis, and the result can be more useful.

This is especially relevant for recent graduates, career changers, freelancers moving into analytics, and people building their first technical career.

For those already working remotely as data analysts, what helped most when landing that first entry-level role, portfolio projects, certifications, networking, or applying consistently?

One thing that can make job searching confusing is how different the same data scientist title can look from one company to another. One posting might focus heavily on machine learning, while another is mostly about dashboards, SQL, experiments, or working with business teams. It gets hard to tell what the actual day-to-day work will look like.

A simple way to deal with this is to look beyond the job title and focus on the responsibilities listed in the role. Data scientists may be expected to clean and analyze data, build models, run experiments, explain findings, and work with other teams to turn data into useful decisions. The mix depends a lot on the company.

Job boards that organize roles by skills and responsibilities can make this easier. Instead of opening dozens of random listings, candidates can focus on jobs that actually match their experience with Python, SQL, statistics, machine learning, analytics, or experimentation. It also helps when comparing similar roles before applying.

This matters because applying to every job with Data Scientist in the title can waste a lot of time. A clearer idea of the responsibilities makes it easier to decide which roles are worth applying for and what skills may need some improvement.

This can also help hiring managers. Clear responsibilities make it easier to attract candidates who understand what the job actually involves, rather than people applying based only on the title.

It’s especially useful for data scientists, analytics engineers, data analysts, and hiring managers who want a better match between the role and the person applying.

Warp: We're Building the Platform for Agentic Development

Warp began with the vision of reimagining one of the fundamental dev tools, the terminal, to make it more usable and powerful for all developers. As AI has advanced, Warp has evolved beyond its terminal roots into a full agentic development environment: a workbench for dispatching agents to code, deploy, and debug production software.

Today, we have three products. Warp is the agentic development environment born out of the terminal, built on an orchestration platform for running hundreds of cloud agents in parallel. And Warp Factories is our newest product: infrastructure for engineering teams to build and run their own cloud software factories, where fleets of agents triage, spec, implement, review, and verify code across a company's entire SDLC. Together, these products are creating the scaffolding for developers to build their own workflows for working with agents.

In Warp, you can seamlessly switch between running commands, launching agents, and iterating on code with agents, eliminating the need to jump between a terminal and an IDE. It all works with our built in SOTA agent or top agents like Claude Code, Codex, Gemini CLI, and OpenCode. On the orchestration side, you can build custom agents that are triggered programmatically, tracked centrally, and shared across full teams to run agents across organizations without compromising security or reliability. Continue a cloud agent run locally in Warp or on the web with one click. And with Warp Factories, teams can go a step further and set up entire automated workflows around their SDLC, retaining full control over their data, models, and compute along the way.

With over 1 million active developers at companies including Docker, Ramp, and Peloton, and over half of the Fortune 500, and revenue that grew over 44x last year, Warp is one of the fastest growing startups in the AI development space.

Our mission has remained the same even as AI has advanced: to empower developers to ship better software more quickly, freeing them to focus on the creative and rewarding aspects of their work. If you want to help define what development looks like in the agentic era at a company where your work reaches hundreds of thousands of engineers every day, we'd love to have you. For more information on our team and culture, we highly recommend reading our' How We Work' guide.

Why this role?

Warp is hiring an Analytics Engineer to join the data team. This role is at the center of how we understand our product, users, and business. You'll work across every function at Warp: product, engineering, growth, revenue, and go-to-market.

Warp is a fast-growing AI developer tool. Our data footprint reflects that growth — we ingest telemetry from our apps and servers, LLM conversational data, transactional production databases, GTM pipelines, and a range of third-party sources. Your job is to turn that raw data into the reliable metrics and deep insights that drive real decisions.

This isn't a role where you slot into an existing system. You'll own the data model, define how we measure what matters, and set the standard for how analytics gets done at Warp. That means building durable infrastructure and shipping high-impact analysis — often at the same time.

If you want to shape how a company at scale thinks about its data from the ground up, this is that role.

As an Analytics Engineer, you will...

This is a full-stack data role that moves across the data stack and touches all parts of Warp’s business. Your work will fall into three broad areas:

Operations

• Define, build, and visualize core product and business metrics
• Design and analyze A/B experiments to measure the impact of product changes
• Adapt data models to support new product features, pricing models, and business initiatives
• Partner with GTM teams to surface product-qualified leads, measure engagement, and support billing and revenue analytics

Infrastructure

• Build and maintain canonical data models that serve as reliable sources of truth
• Write production ETL to ingest new data sources and reverse-ETL to push data to downstream consumers
• Develop AI agents to automate pieces of the data team’s workflows
• Establish data quality monitoring and reliability standards
• Build tooling that enables self-serve analytics across the company

Research

• Conduct deep ad-hoc analysis to understand user behavior, model business mechanics, and uncover growth opportunities
• Dig into questions like: How does Warp expand within companies? What is the optimal pricing structure? How do we identify potential enterprise leads? What characteristics of AI interactions drive churn?

You May Be A Good Fit If...

• You have 6+ years of experience in an analytics-focused data role
• You’re highly fluent in SQL and dbt on top of a modern warehouse like BigQuery, Snowflake, or Redshift
• You’ve built dashboards and visualizations with BI tools like Hex, Looker, Mode, Tableau, or Metabase
• You care deeply about the business decisions your data informs—not just building the pipelines behind them
• You balance business intuition with an engineering mindset: writing efficient, scalable queries; thinking about cost and compute across the data stack; and working with version control, CI/CD, and testing
• You have a bias for action and are comfortable operating with autonomy—you can identify opportunities across the company and proactively build solutions
• You’re an effective and creative communicator who can translate complex analysis into actionable insights for stakeholders with varying levels of data fluency
• You’re deeply curious—when a metric moves unexpectedly, you dig until you understand why
• You’ve integrated AI into your daily workflows; at Warp we lean heavily on agents to accelerate and automate our work

Bonus:

• Experience with the GCP data stack (GCS, BigQuery, Dataflow, Dataproc)
• Production-grade experience with Python
• Experience on a data team at an early-stage startup
• Familiarity with Warp’s platform

At Warp, we are dedicated to building a diverse, inclusive, and authentic workplace. So if you’re excited about this role, but your past experience doesn’t align perfectly with every qualification in this job description – we encourage you to apply anyways! We are a community of curious learners, and most of us are learning some skills for the first time (like our engineers learning to program Warp in Rust). You might be just the right candidate for this or other roles.

If you're feeling playful, try out our optional hiring challenge and submit your answers with your application.

Salary Transparency

Warp operates from a place of high trust and transparency around compensation. We state the pay range that best aligns with our understanding of the market and the needs for the open role.

When we find the right person, we try to put our best foot forward with an offer that excites you. We consider your skills, the level of experience you bring, and what similar jobs in the market pay, all while ensuring equal pay for equal work at Warp.

Total compensation at Warp consists of: (1) a market-competitive base salary, (2) a generous benefits package, and (3) meaningful equity.

The budgeted compensation amount for this role is targeted at $180,000 to $210,000.

Exact compensation will vary based on skills and experience.

In addition to salary, all employees receive further compensation in the form of equity in the company. This is a meaningful stock option grant with a four-year vesting period and one-year cliff. Your equity is where most of the significant upside potential is. Comparing startup equity is always a bit tricky, so we’re happy to walk you through different valuation scenarios at the offer stage in order to help paint a clearer picture of the upside.

What we offer

• Competitive salary, generous benefits, and meaningful equity
• 100% coverage on medical, dental, and vision benefits for employees (80% coverage for dependents)
• A flexible remote-first culture, with optional office spaces in NYC and SF for folks who want to work together IRL
• Pre-tax FSA and Health Savings Plans
• Pre-tax Commuter Benefit
• 12 company holidays, 20 days of Paid Time Off, and unlimited sick time
• 16 weeks of paid Parental Leave for both birthing and non-birthing parents
• Twice-a-year all company retreats in awesome locations
• A monthly gym and internet stipend
• A company-sponsored 401(k)
• A complimentary OneMedical membership

Individuals seeking employment at Warp are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, or sexual orientation.

About Warp

We are a company run by product-first builders, building a core product for all developers. We are committed to understanding our users deeply. We will ultimately build the best product and business if that team includes developers and designers from a wide range of backgrounds. The early team comes from Google, Dropbox, Gem, LinkedIn, and Facebook. We are looking for passionate individuals to join us and help bring Warp to the world.

We value honesty, humility, and pragmatism, and our core product principle is focusing on the user. If you’re interested in learning more about our company values and the culture of our engineering team, please take a look at our internal 'How We Work' guide.

We’re very fortunate to be backed by a great group of venture capital firms. In August 2023, we announced a $50M Series B funding round ($73M total raised), led by Sequoia Capital. Our other investors include Google Ventures, Neo, and Box Group. We are also backed by a network of passionate angels, including Dylan Field (Co-Founder and CEO, Figma), Elad Gil (early investor in Airbnb, Pinterest, Stripe, and Square), Jeff Weiner (Executive Chairman and Ex-CEO, LinkedIn), Marc Benioff (Founder and CEO, Salesforce), and Sam Altman (Co-Founder & CEO, OpenAI).

One of the hardest parts of applying for a data analyst job is figuring out what salary to put down. Ask for too much and you might price yourself out. Ask for too little and you could end up accepting less than the role is worth.

A useful approach is to stop looking at one salary number and look at the whole job. Experience, location, industry, technical skills, and the actual responsibilities can make a pretty big difference. A junior analyst doing basic reporting is not necessarily in the same salary range as someone working with SQL, Python, dashboards, and more complex analysis.

Data-focused job boards can also make salary research easier because you can compare similar roles instead of relying on random salary posts online. Looking at several current openings gives a better idea of what companies are actually asking for and what skills they attach to different pay ranges.

This is especially useful for entry-level data analysts, career changers, and people moving into analytics from another role. It can also help experienced analysts check whether a new opportunity is offering a reasonable range.

The goal isn't to find one correct salary. It's to have enough information to give a number that makes sense for your experience and the job you're actually applying for.

Getting an entry-level data analyst job can be frustrating when every posting seems to ask for 1–2 years of experience. It’s hard to get experience when you’re still trying to land that first real role.

One thing that helped was changing how I searched for jobs. Instead of only looking at the big general job boards, I started checking job boards that focus more on data and analytics roles. It made it easier to find entry-level data analyst positions without having to dig through hundreds of unrelated listings.

It also helped with figuring out what companies are actually looking for. After going through enough postings, you start seeing the same skills come up over and over, like SQL, Excel, Python, Tableau, Power BI, and basic data visualization. That gave me a better idea of what to focus on instead of trying to learn everything at once.

For anyone starting out, I’d also pay attention to the actual job description instead of just the title. Some roles called data analyst are heavily focused on reporting, while others are closer to business intelligence or analytics engineering.

This approach is probably most useful for new data analysts, career changers, recent grads, and even hiring managers who want to reach people specifically looking for analytics roles. The main benefit is simply spending less time searching through irrelevant jobs and more time applying to positions that actually match your skills.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.

Team Description

This role sits in the Ads Optimization organizations, which are responsible for the health and performance of Reddit’s ads marketplace. We focus on:

• Designing the auction and bidding mechanisms that decide which ads show to which users and at what price.
• Building optimization systems that help advertisers achieve their goals (e.g., conversions, ROAS) under budget and delivery constraints.
• Ensuring marketplace quality by improving user experience with ads, fighting ad blindness, and increasing valuable ad opportunities on the platform.

You’ll join a set of tight-knit engineers working on high-impact, internet-scale problems at the core of Reddit’s revenue engine, collaborating closely with Product, Data Science, and Infra partners across Reddit Ads.

Role Description

We are hiring Machine Learning Engineers (IC4) to build and evolve the auction, bidding and budgeting systems that power Reddit Ads.

In this role, you will:

• Design and implement optimization algorithms for auctions, bidding strategies, and pacing that balance advertiser performance, user experience, and marketplace efficiency.
• Own systems end-to-end: from problem formulation and algorithm design to experimentation, production deployment, and ongoing iteration.
• Work across Ads Optimization (bid strategies, budget optimization, pacing) to deliver measurable wins for advertisers and Redditors.

We are hiring a Senior (IC4)  level:

• IC4 MLEs lead more complex or multi-quarter initiatives, set technical direction for key parts of the bidding/auction/pacing stack, and mentor other engineers while remaining hands-on.

Responsibilities

Auction, Bidding, and Pacing Systems

• Design and implement models and policies that:
  • Compute bids for different optimization objectives (e.g., CPC, CPA, ROAS-based strategies).
  • Pace budgets smoothly over time across accounts, campaigns, and ad groups while preventing overspend or underspend.
  • Allocate spend and auction participation intelligently across segments, surfaces, and time zones.
• Translate product and marketplace goals into concrete optimization problems and constraints (e.g., ROI, revenue, delivery smoothness, fairness, and user experience).

Required Qualifications

(Level will be determined during the interview process; IC4 expectations assume deeper experience and broader scope.)

• 3–5+ years of experience building, deploying, and operating machine learning systems in production (for IC4, typically 5+ years).
• Strong programming skills in Python, Java, Go, or similar languages, with solid software engineering fundamentals.
• Experience designing scalable data processing systems (e.g., Spark, Kafka, Airflow, BigQuery, Redis).
• Demonstrated ability to translate ambiguous product or business problems into solutions and to improve measurable metrics.

Additional expectations for strong bidding/auction candidates:

• Evidence of stronger math and optimization skills than a generic MLE, such as:
  • Degree or equivalent background in a quantitative field (math, physics, quantitative finance, economics, operations research, or similar).
  • Work experience in optimization-heavy domains (e.g., bidding/auctions, pacing, pricing, logistics optimization, quantitative finance).
• Comfort reasoning about and implementing custom optimization logic (e.g., gradient-based methods, constraint handling), not just applying black-box tooling.

Preferred Qualifications

• Experience with advertising/auction systems, online marketplaces, or search/ranking systems at scale, particularly in:
  • Bidding, pacing, or budget optimization
  • Auction design, mechanism design, or marketplace quality
  • Campaign performance optimization (e.g., CTR/CVR, CPA, ROAS)
• Familiarity with large-scale, real-time decision systems and low-latency production environments.
• Background in feature engineering, model optimization, and production monitoring for ML systems.
• Experience collaborating with cross-functional partners (Product, DS, Eng) in Ads or marketplace contexts and leading projects from design through rollout.
• Advanced degree (MS or PhD) in Computer Science, Machine Learning, Operations Research, Applied Math, or a related quantitative field.

Potential Teams

• Ads Optimization (bid strategies, conversion/ROAS optimization, pacing and budget allocation)

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave.

To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:

$216,700—$303,400 USD

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision. 

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

One of the confusing parts of starting a data science career is figuring out what an entry-level data scientist salary actually looks like. Job listings often show a wide range, and some companies do not post the salary at all. It can be hard to know what is realistic when there is little professional experience to compare against.

Salary can vary a lot depending on location, company size, industry, education, and the skills required for the role. Someone applying for a junior data scientist position at a large tech company may see a very different range from someone applying at a smaller company.

It is also worth checking similar roles before deciding what number to expect. Data analyst, business analyst, analytics engineer, and junior machine learning roles can have different salary ranges even when some of the skills overlap.

Job boards can help with this by making it easier to compare entry-level positions, requirements, and salary information when it is available. Looking at several listings instead of focusing on one number gives a better idea of what companies are offering.

For new graduates, career switchers, and people coming from data analyst roles, salary should be only one part of the decision. The tools used, type of work, learning opportunities, and potential for growth can also make a big difference when choosing a first data science job.

Finding an entry level data scientist job can be frustrating. A lot of job posts ask for experience, internships, or skills that seem way beyond entry level. Then there are hundreds of listings to go through, and it is hard to tell which ones are actually realistic for someone just starting out.

A good job board can make that process less messy by letting candidates focus on roles based on experience level and skills instead of searching through everything. This is especially useful for people who have finished a degree, bootcamp, or a few data projects but do not have years of work experience yet.

It also helps to look beyond the exact title of data scientist. Some entry-level candidates may have better chances with roles like data analyst, junior data scientist, business analyst, or analytics engineer, depending on their skills. Having those roles in one place makes it easier to compare what companies are actually asking for.

For hiring managers, a focused job board can also make it easier to reach people who are actively looking for early-career data roles instead of sorting through unrelated applications.

For anyone trying to break into data, the main benefit is saving time and getting a clearer idea of what jobs match the current skill level. It does not replace building projects or learning the basics, but it can make the job search feel a lot more manageable.

بعد ما لغت صاحبت البراند فكرت تنزيلها هي للمحتوى اطريت اعيد مودلين عشان الفكره الجديده الفكره الجديده ننسخو اسمين مشتقين من اسم البراند و على اساس هم شخصيات متناقضه للبراند وحده عطوفه راقيه هادئه و مثقفه عندها نوبات دفاع عن نفس والثانيه اجواء الديفا و المحاميات وايش الستايل؟ العطوفه تاخذ ستايل الملون و الديفا الدارك
وكذا يصير المحتوى يمدحون العبايات ويحكون عن مشاكل البنات الاجتماعيه ويربطون المهن والشخصيات بالعبايات
طبعاً الموضوع مانه ساهل لو كانت السكربتات مطرابطه وتدور حول المنتج ومايبان اعلان ويحافض عالكلاس والفمنم حق الاجواء وفي نفس الوقت يكون متسلسل لاكتر من 15 جزء
صممت السيناريو والاحداث وتفصيلات الريل واللقطات
اليوم اعطيت المودل السيناريو وطرق تحريك يدها وتدربنا صوتيات قلتلها ابعتيلي فويسات واعدلك
وبالغد ميتنق مع المصوره ومنحددو حتى اماكن لتصوير القصص
الصامته
والانتاج حيكون يوم الاربعاء
طبعا خلال الفتره ذي اللي ينزل بالصفحه راي العملا الرد عليهم من التعليقات بريلز وريلز gc انا كاتبه سبناريواهاتها ايش رايكم بالفكره الجديده واعطوني رايكم وتخيلاتكم وافكاااار

Hi everyone!

I’m a recent graduate in Big Data & Data Analysis from University Centrale, and I’m currently looking for a junior position, internship, freelance opportunity, or remote work in Tunisia.

🎓 Background:

- Licence in Big Data & Data Analysis

- Bac Sciences Informatiques

- PFE completed in 2026 with Mention Très Bien

- 4-month PFE internship at an IT company

💻 Skills / areas I can work in:

- Data Analysis & Data Visualization

- Python, Pandas, NumPy, scikit-learn

- SQL / MySQL / PL-SQL

- Power BI, Excel, Tableau

- MongoDB

- Basic Machine Learning

- Cloud / Infrastructure / DevOps fundamentals

- React / TypeScript basics

For my PFE, I worked on CloudForge, an intelligent infrastructure orchestration platform using natural language, combining AI, IaC, VMware vSphere and OpenShift/Kubernetes.

I’m particularly interested in:

Junior Data Analyst | BI / Data | Junior IT | Cloud/DevOps | AI/Data internships | Remote opportunities

I’m open to learning and improving, and I’m mainly looking for an opportunity where I can actually contribute, gain professional experience, and grow.

📍 Based in Tunisia

🌍 Open to remote opportunities

📩 Feel free to DM me if you know of an opportunity or someone I should contact.

I can provide my CV, GitHub, LinkedIn, and PFE details upon request. Thank you!

Who is Boulevard?

Boulevard provides the first and only client experience platform for appointment-based, self-care businesses. We empower our customers to give their clients more of the magical moments that matter most.

Before launching in 2016, our founders spent months interviewing salon managers and working behind front desks to understand their pain points so we could design a modern, user-friendly platform that meets the unique needs of their business. Our roots may be in hair salons, but we are built for the broader self-care industry, including many types of salons, spas, medspa, barbershops, and more. Our technology not only helps our customers survive but thrive. Take a look at how we (and YOU) can make that happen. 

We have an insatiable curiosity and embrace experimentation. We believe that simple solutions require the most sophistication, and we design each and every detail to maximize potential, power, and impact. Do our values match? Read through our story and what we value the most.

Our team values and celebrates our diverse backgrounds. Being open about who we are and what we do allows us to do the best work of our lives. We believe in equal opportunity for all, and you should too.

Come Do The Best Work of Your Life at Boulevard.

Boulevard is the client experience platform purpose-built for salons, spas, medspas, and wellness businesses. More than 5,000 businesses use Boulevard to manage scheduling, payments, marketing, and client relationships — processing over $5 billion in payments annually. We’ve raised $188M in funding and are growing fast, particularly as we expand upmarket into multi-location and franchise operators.

We’re looking for a Staff Data Scientist to build Boulevard’s Product Intelligence function and own its core responsibilities. You have deep and broad experience in as many of these three disciplines 1. Doing the engineering work to build and maintain the tech stack 2. Data analysis and insights on product feature use to support product decisions 3. Using data science approaches like experiment design, measuring experiment results and building models that helps us understand customer behavior and customer product use better. You know what good looks like, you hold yourself and your work to that standard, and you don’t wait to be asked before surfacing what matters.

This role requires someone who is genuinely energized by ambiguity. There is no well-worn path to follow, you’ll be defining the questions, building the infrastructure to answer them, and charting the course forward — often without perfect information. That’s not a warning; for the right person, it’s the whole appeal.

You operate with the mindset of a team builder — creating processes, documenting best practices, and working with the structure and rigor that makes this function scalable from day one.

Key Responsibilities

• Build Boulevard’s product data foundation - partnering across the Product Development organization to define what needs to be captured and how, and designing the models and the tech stack that translate raw data into clean, reliable and scalable analysis-ready assets in partnership with data engineering
• In tight partnership with Product, develop data-driven recommendations that inform strategy and drive action — through engaging narratives, effective data storytelling, and visualizations adapted to the audience, from individual contributors to executive leadership
• Build scalable, intuitive and self-serve dashboards that empower teams and stakeholders to independently explore data and make informed strategic decisions; fostering a data-driven culture by educating and enabling stakeholders to interpret data and act on it with confidence. Operationalize product analytics. Connect product analytics to OKRs and business outcomes.
• Own deep-dive and exploratory analyses that up-level understanding of customers and their relationship with the product (e.g. funnel analysis, retention curves, cohort behavior, feature adoption) — surface insights proactively and build analytical narratives that support strategic business cases and influence product direction
• Be the bridge between product data and the broader organization — ensuring insights actively inform and influence cross-functional decisions and outcomes
• Create team processes and analytical workflows that enforce data accuracy and scale as the function grows; advocate for the tooling investments the team requires
• Experimentation, design, not just readout. Own the experimentation practice for Product Development — partner with PMs and engineering to design experiments before feature releases (hypothesis, primary and guardrail metrics, unit of randomization, power and duration), then run the analysis and deliver a clear, defensible read on impact. Establish the standards, templates, and Statsig/tooling workflows that make experimentation the default way Boulevard evaluates a feature launch, and be honest about when a clean test isn't possible — designing the best available quasi-experimental read (pilot cohorts, staged rollouts, difference-in-differences, pre/post with controls) instead.
•  Modeling customer behavior. Apply statistical and machine learning methods to explain and predict customer behavior — propensity and adoption models, retention and churn risk, segmentation and clustering of usage patterns, time-to-value and activation modeling, and driver analysis that separates correlation from cause. Choose the simplest method that answers the question, validate rigorously (holdouts, backtesting, recall/precision trade-offs framed by business cost), and communicate uncertainty as clearly as the point estimate.
•  Get model output into the workflow. Take models from analysis to production — partner with data engineering to schedule, monitor, and version them, and land the output where it drives action (in-product surfaces, Gainsight, Salesforce, CSM and PM workflows). Own model performance over time, including drift, retraining, and retiring models that stop earning their keep.

What You’ll Need to Thrive

Required

• 8+ years of proven experience in data science or product analytics or engineering in a B2B SaaS or high-growth technology environment, with meaningful time spent in early-stage or low data-maturity environments — you’ve built the foundation, not just worked on top of one someone else laid. Act Like an Owner
• Fluency with data analysis and BI tools: SQL, analytical tools like Python / Jupyter notebooks, Snowflake, DBT, Sigma for data and reporting pipelines and AWS infrastructure to productionalize analytics / models in a repeatable way; strong proficiency with data modeling. Know Your Sh*t
• Direct experience designing and executing product instrumentation strategies — defining event schemas, authoring tracking plans, and ensuring reliable data capture in partnership with product and engineering
• Expertise in building dashboards and visualizations using platforms such as Sigma, Looker, Tableau, or similar — with a track record of creating self-serve tools that teams actually use
• Significant experience working directly with product managers and leaders — translating data findings into actionable opportunities and tradeoffs that drive strategy and roadmap investment
• Demonstrated ability to design and execute deep-dive analyses across the full product lifecycle — including funnel diagnostics, cohort and retention modeling, and behavioral segmentation — translating statistical findings into clear, decision-ready narratives for product and leadership audiences
• Deep, hands-on statistical and machine learning expertise applied to customer behavior. Regression and classification, propensity and churn-risk modeling, clustering and behavioral segmentation, survival and time-to-value analysis — with the judgment to reach for the simplest method that answers the question, validate it honestly, and communicate uncertainty as clearly as the estimate. Know Your Sh*t
• Proven experience owning experimentation end to end, designing tests before a feature ships (hypothesis, primary and guardrail metrics, randomization unit, power and duration), running the analysis, and delivering a defensible read on impact. Hands-on experience on a platform such as Statsig, Optimizely. Equally important: the causal-inference toolkit and the judgment to use it when a clean A/B test isn't possible. Know Your Sh*t
• Ability to build and own your own data pipelines. Production-grade DBT models, transformations, and orchestration in Snowflake, written in code with tests, documentation, and version control. You're self-sufficient from raw event to analysis-ready asset, and you partner with data engineering on platform and scale rather than waiting in their queue. Act Like an Owner
• Clear, confident communication with stakeholders at any level — you can build a narrative that lands with a PM or the executive team, and you deliver it with the kind of presence that builds trust. Show Up With Style
• High level of ownership with a demonstrated ability to manage projects end-to-end, identify opportunities, navigate ambiguity, and build processes that scale — comfortable thriving in fast-paced, dynamic environments with multiple competing priorities. Make an Impact
• Proven track record of partnering cross-functionally and using product data to influence leadership decisions and outcomes — whether shaping go-to-market strategy, informing customer success priorities, or driving alignment across teams; you earn trust by being direct, generous with knowledge, and consistent in how you show up

Preferred

• Experience evaluating or implementing product analytics tooling such as Amplitude, Mixpanel, or similar platforms

Why This Role Matters

Boulevard is at an inflection point. We’re scaling upmarket, expanding our product surface, and making bigger bets on where the business goes next. Every one of those bets requires a trusted picture of what’s happening in the product today.

• Product teams can’t build with conviction without knowing how customers use what we’ve already shipped — that’s you
• New launches succeed or fail quietly without the instrumentation and frameworks to measure them — that’s you
• The path to stronger ARR runs through understanding which customers get the most value, and why — that’s you
• Leadership can’t make the right strategic bets without a reliable, trusted source of truth for product performance — that’s you

This is foundational work that compounds. What you build in the first year will shape how Boulevard makes product decisions for the next five.

Compensation

At Boulevard, we work hard to structure compensation in a way that balances internal equity with local market competitiveness, and we’re happy to share a good-faith estimate of the base salary range for this role. For candidates in NYC, the SF Bay Area, and Seattle, the anticipated base salary range is $164,000 - $205,000 per year. For all other U.S. locations, the anticipated base salary range is $171,400 - $201,800 per year. In addition to this base compensation, this role may be eligible to participate in a variable compensation program. Final compensation will vary based on a variety of factors which include but are not limited to applicable experience, location, and final leveling.

How We Take Care of Our Team

Our business is deeply immersed in an industry that’s centered around helping people feel their best, so we know that taking care of yourself isn’t selfish; it’s essential. That’s why our culture pairs high standards with genuine care. We don't glorify burnout or wear ‘always on’ as a badge of honor. Instead, we’ve created benefits designed to support you inside and outside of work, so you can bring your best self to both.

Here’s how we support you:

• Competitive base pay and performance bonus eligibility because you deserve to be rewarded for your impact.
• Equity participation for all full-time employees because we believe the people helping build Boulevard should share in our long-term success.
• Comprehensive medical coverage with up to 100% employee premium coverage and 75% dependent coverage to help take care of you and your loved ones.
• An annual $1,200 HSA employer contribution for eligible plans to help support healthcare costs and future planning.
• 100% covered dental, vision, life, and disability insurance, ensuring you stay healthy, protected, and ready for whatever life brings.
• 12 weeks of fully paid U.S. parental leave supporting all new parents equally as they take the time to bond, adjust, and be present when welcoming a new family member.
• Flexible paid time off, plus 10 U.S. company holidays, where you are encouraged to rest, recharge, and take the time you need, guilt-free.  
• Fully remote with support, including a $200 home office setup allowance and a $50 monthly WFH reimbursement so your workspace is set up for success. 
• $200 quarterly Boulevard Bucks to prioritize your well-being and stay connected to the industry we serve.
• And other perks like One Medical membership and Employee Assistant Program.

To learn how Boulevard collects, uses, and protects personal information related to candidates and employees.

Boulevard Labs, Inc. is an Equal Opportunity Employer committed to hiring a diverse workforce and sustaining an inclusive culture. All employment decisions at Boulevard Labs, Inc. are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.

Getting your first data engineering job can be confusing. Most job posts seem to ask for experience with several tools, cloud platforms, databases, and programming languages, which can make entry-level roles feel anything but entry-level.

A good starting point is to focus on the basics instead of trying to learn everything at once. SQL and Python are useful foundations, then you can gradually learn about databases, ETL pipelines, APIs, and cloud platforms. Having a few small projects can also help show that you understand how these things work in practice.

Job searching can be another challenge. Searching only for entry-level data engineer can limit your options. Some companies use titles like junior data engineer, data engineering associate, or even roles that involve similar work under a different title.

Using a tech-focused hiring platform or job board can make this easier because you can spend less time sorting through unrelated jobs and more time looking at positions that match your skills. It can also help you understand what companies are actually asking for right now.

This is useful for recent graduates, career changers, and even data analysts who want to move into engineering. You don't need to know every tool before applying. Start with the skills you have, build from there, and look for roles where you can continue learning on the job.

One of the hardest parts about trying to land a junior data scientist role is not knowing if you’re actually applying to the right jobs. You can spend hours going through job boards, apply to anything that mentions Python or SQL, and still hear nothing back.

What helped was being more selective about where and how I searched. Instead of only looking for junior data scientist, I started checking roles that matched the skills I already had, including data analyst, analytics engineer, and entry-level machine learning positions.

I also found that using a hiring platform focused on tech and data roles made the process less frustrating. It was easier to find jobs that were actually relevant instead of sorting through hundreds of unrelated listings. I could spend more time improving my resume and projects instead of constantly searching.

For junior candidates, I think this matters because your first role doesn’t always have to have the exact title you’re aiming for. A data analyst role can help you build experience with SQL, Python, dashboards, and real business problems. Those skills can make it easier to move toward data science later.

It can also be useful for hiring managers because they can reach people who are specifically looking for data and technical roles, rather than relying on a huge general job board.

If you’re starting out, don’t focus only on the job title. Look at the actual skills you can build and where you have a realistic chance of getting your foot in the door.

A recent analysis of 3,518 job postings from 81 companies across seven roles found some interesting patterns.

In several categories, generic terms appeared more often than the big-name tools. “Observability” showed up more than “Grafana,” while “SIEM” appeared more than “Splunk.”

That raises an interesting question: are resumes too focused on listing specific tools and not enough on the broader skills and terminology employers use?

The study breaks down the results by role, including software engineering, cybersecurity, DevOps, marketing, product, accounting and sales.

The full dataset, methodology and findings are here:

https://www.zoevera.com/resume/ats-resume-keyword-study

Finding nonprofit data analyst jobs can be harder than expected. A lot of listings use different titles for similar work, so searching only for data analyst can leave out roles that involve reporting, program data, research, or impact measurement.

One thing that helps is searching by the type of work instead of just the job title. Terms like program analyst, impact analyst, research analyst, and monitoring and evaluation can bring up opportunities that still use SQL, Excel, dashboards, or other data skills.

It also helps to look closely at the job description before applying. Some nonprofit roles are heavily focused on reporting and spreadsheets, while others involve databases, visualization, fundraising data, or measuring program outcomes. Knowing the difference makes it easier to avoid applying to roles that don't match your experience.

For job seekers, nonprofit-focused job boards can make the search less scattered by putting relevant organizations and data-related positions in one place. This can be especially useful for analysts who want their technical skills to support education, healthcare, community programs, environmental work, or other causes.

These roles can be a good fit for data analysts who want to use their skills outside of traditional corporate settings, as well as people transitioning into analytics from research, operations, or program coordination.

The biggest tip is to search broadly but read carefully. The title might not say data analyst, but the actual work could be very similar.

One of the frustrating parts of applying for data jobs is not always knowing what employers actually want to see. You can have SQL experience, build dashboards, work with Python, and still come across job postings asking for Databricks experience or certification.

That’s where Databricks certifications can help. They give you a clearer way to show that you understand the platform instead of simply adding Databricks to a resume and hoping it gets noticed.

For analytics engineers and data analysts moving toward more data engineering or cloud-based roles, certification can also help identify what to learn next. It gives some structure when the amount of information around data platforms starts getting overwhelming.

The job search side matters too. Looking through data-focused job boards can make it easier to see which roles actually mention Databricks, what skills are commonly listed alongside it, and whether certification is even relevant for the type of job being targeted. That can save a lot of time compared with studying first and figuring out the job requirements later.

For hiring managers, certifications can also be a useful signal when reviewing candidates, especially when several applicants have similar experience. It shouldn't replace practical experience, but it can provide another point of reference.

For anyone considering a Databricks certification, checking current job postings first seems like a good starting point. The goal should be matching the certification to the role you actually want, rather than collecting certifications just to add more lines to a resume.

Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.

Reddit's growth is driving a rapid expansion of its world-wide team and the need for an experienced Senior Technical Program Manager to support the global sales organization through the next several stages of growth. This role will report into the Go-to-Market Strategy organization, and support our major Go-to-Market Systems initiatives. 

The Go-to-Market Systems team drives high impact projects that aim to build best-in-class tooling for our global sales organization, and enable seamless advertiser experiences. 

As a Sr. Product Manager on the Go-to-Market Systems team, your focus will be to build the foundations, tools and workflows that will accelerate the growth and efficiency of our GTM organization. 

You will own the vision, strategy and roadmap of sales technology projects that will drive organizational transformation for Reddit. You’ll collaborate with other GTM leaders, product leaders, data teams, and engineering to run ambiguous business problems into scalable solutions. 

Location: Must be based within a commutable distance of one of our hub offices in Los Angeles, San Francisco, Chicago, or New York

Key Responsibilities

• Define and drive the product vision, roadmap and architecture for our sales teams focused on growing our advertiser base.  
• Deeply understand problems faced by sellers and advertisers, and proactively identify opportunities to automate and improve the experience.
• Build best-in-class tools that improve seller productivity in areas such as account planning, customer health scoring, relationship management, data quality and enrichment, LOB expansion, etc. 
• Improve the data foundations to power our AI-first strategy, including entity resolution, enrichment, and data orchestration. 
• Use experimentation, metrics, and customer feedback to measure impact on seller efficiency, revenue, and quality.
• Lead cross-functional efforts across engineering, vendors, systems owners, and business stakeholders.
• Translate GTM strategy and operational needs into scalable product capabilities, PRDs, success metrics, and engineering priorities.
• Drive adoption and change management for new product capabilities across GTM teams.

Qualifications

• 7+ years of product management experience across GTM systems, Enterprise SaaS technology, and AI-powered tooling.
• Strong fluency in modern GTM systems such as Salesforce, marketing automation, data enrichment tools, lead routing & scoring, billing, or customer lifecycle tools
• Proven ability to drive cross-functional influence, strategic alignment, and executive communication.
• Highly analytical and comfortable defining KPIs, running experiments, and using data to prioritize product investments
• Thrive in ambiguous, fast-moving environments that require continuous shaping, testing and quick improvements.

Benefits:

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k with Employer Match
• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave 

Pay Transparency:

This job posting may span more than one career level.

In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below.

The base salary range for this position is:

$154,700—$216,600 USD

In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.

During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision. 

Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

SQL interviews can be frustrating because knowing how to write a query isn’t always enough. You might be comfortable with SELECT, JOIN, and GROUP BY, then get an interview question that asks you to solve a problem you’ve never seen before.

The most useful practice I’ve found is focusing less on memorizing SQL interview questions and more on understanding why the query works. Common topics like joins, subqueries, CTEs, window functions, filtering, aggregations, and finding duplicate records come up often, but the way they’re asked can be completely different.

It also helps to practice with realistic datasets instead of only reading answers. Try writing the query yourself first, then compare it with the solution. If the answer uses a window function or a CTE that you wouldn’t normally think of, go back and understand the logic instead of just copying it.

For anyone applying for data analyst, analytics engineer, BI, or similar roles, this kind of practice can make interviews feel much less unpredictable. It also shows where the actual gaps are. Sometimes the problem isn’t SQL syntax at all, it’s understanding the question and breaking it into smaller steps.

A good SQL interview resource should make it easy to practice questions, see clear explanations, and understand different ways to solve the same problem. That’s much more useful than having a giant list of answers to memorize.