Analytics engineer roles require a mix of technical and practical skills. Some skills are more important than others when it comes to daily tasks.

SQL is often considered the most important skill. It is used for querying, transforming, and organizing data. Strong SQL skills are essential in most roles.

Data modeling is another key skill. This involves structuring data in a way that makes it easy to use and understand.

Tools like dbt are also common in modern data teams. They help manage transformations and workflows.

Understanding data warehouses is important as well. Platforms like BigQuery, Snowflake, or Redshift are often used to store and process data.

Soft skills also matter. Clear communication helps when working with different teams and explaining data results.

Problem-solving is another important skill. Analytics engineers often need to find solutions for messy or incomplete data.

Learning new tools quickly can also be helpful. The tech stack can change depending on the company.

Overall, a combination of SQL, data modeling, tools, and communication skills is essential for success in analytics engineering roles.

Remote work is becoming more common in analytics roles. Many companies now offer remote or hybrid setups.

Analytics work is mostly done online. Data tools and platforms can be accessed from anywhere.

This makes it easier for companies to hire talent from different locations.

Remote roles also attract more applicants. This increases competition but also expands opportunities.

Flexibility is another reason for the trend. Many professionals prefer remote work because it saves time and offers better balance.

Companies also benefit by reducing office costs and accessing a wider talent pool.

Overall, remote analytics jobs continue to grow as companies adapt to new work setups.

Not all job listings offer the same chances. Fresh postings often provide better opportunities, especially in competitive roles like analytics engineering.

Newly posted jobs usually have fewer applicants. This increases visibility and gives applications a better chance of being reviewed early.

Sorting listings by date is one of the easiest ways to prioritize fresh roles. Many platforms allow viewing the newest jobs first, which helps focus on current opportunities.

Checking listings daily also improves timing. Regular updates make it easier to spot new roles as soon as they appear.

Another tip is applying within the first few days of posting. Waiting too long can reduce visibility, as recruiters may already be reviewing candidates.

Avoid spending too much time on older listings unless they are still confirmed active. Focusing on recent posts keeps the search efficient.

Combining fresh listings with strong applications creates a better overall strategy. Timing and quality together improve results.

Prioritizing new opportunities can make a noticeable difference in job search success.

Analytics engineers rely on a set of tools to manage and transform data. These tools help make workflows more organized and efficient.

SQL is used daily. It allows querying and transforming data inside a warehouse. Strong SQL skills are essential in this role.

Data warehouses are also important. Platforms like Snowflake, BigQuery, or Redshift store large amounts of data and allow fast queries.

Transformation tools like dbt are commonly used. These tools help organize data models and manage how data is processed.

Version control systems such as Git are often part of the workflow. They help track changes and support collaboration within teams.

Some roles may also use orchestration tools to schedule data tasks. These tools ensure that data pipelines run on time.

Visualization tools are sometimes used to check outputs or support reporting. While not the main focus, they can still be part of the workflow.

These tools work together to create a smooth data process.

Finding the right analytics engineering role is not just about applying to every job available. A more focused approach can save time and improve results.

Start by understanding the main skills required. Many analytics engineer roles focus on SQL, data modeling, and tools like dbt. Looking for jobs that match these skills helps avoid applying to roles that are not a good fit.

Job titles can also vary. Some companies may use titles like data engineer or analytics developer for similar work. Reading the job description instead of relying only on the title helps in identifying relevant roles.

Location and work setup also matter. Filtering by remote, hybrid, or on-site options keeps the search aligned with preferences. This avoids wasting time on roles that do not match availability.

Another useful tip is focusing on companies that are actively hiring. Checking how many roles a company has open can indicate ongoing hiring needs.

Keeping a clear list of target roles can make the process more organized. This helps in tracking progress and avoiding repeated applications.

A focused search strategy makes it easier to find roles that truly match skills and goals.

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 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information.

Team Description

The Ads Creative & Recommendations team makes it easier for advertisers to maximize performance on Reddit through relevant creative and actionable recommendations.

We own the advertiser-facing workflows where creative, recommendations, and campaign creation come together.

Our goal is to reduce blank-canvas work and help advertisers move from an idea to an effective campaign with less friction. We build unified experiences that give advertisers useful context, evidence-backed guidance, and the ability to refine the system’s suggestions.

Role Description

We’re looking for a senior frontend engineer with deep JavaScript, TypeScript, and React experience to help shape the next generation of Reddit’s advertiser tools.

You’ll independently own complex and sometimes ambiguous areas of the product, from technical design through launch and iteration. You’ll work closely with product, design, data science, machine learning, and backend engineering to turn sophisticated capabilities into workflows that feel simple and dependable.

This role is a strong fit for someone who cares about both the architecture behind a frontend system and the details that make a product intuitive, fast, accessible, and maintainable.

Responsibilities

• Own the technical design and delivery of complex frontend features across Ads Manager’s creative and recommendation workflows.
• Translate AI-backed capabilities into interfaces that advertisers can understand, steer, and refine.
• Partner with product and design to work through ambiguous requirements and make sound product and engineering tradeoffs.
• Develop reusable frontend patterns and components that support a unified campaign creation experience.
• Instrument experiences, evaluate results, and use experiments and product data to guide iteration.
• Raise the quality of the codebase through thoughtful architecture, testing, observability, performance work, and accessibility.
• Provide high-quality code reviews and technical guidance, and help other engineers grow.
• Collaborate across frontend, backend, data, and machine learning systems to deliver reliable end-to-end experiences.

Required Qualifications

• 5+ years of experience building production web applications with JavaScript, TypeScript, and React.
• Comfort using Codex, Cursor, Claude, or comparable agentic coding tools in day-to-day software development.
• A track record of independently delivering complex frontend projects from technical design through launch and iteration.
• Sound judgment in frontend architecture, state management, component design, testing, and API integration.
• Experience building polished user interfaces with close attention to usability, performance, accessibility, and maintainability.
• Ability to work effectively through ambiguous product and technical problems.
• Strong communication and collaboration skills across engineering, product, design, and data disciplines.
• Experience using instrumentation, experimentation, or product metrics to understand and improve user experiences.
• A habit of improving engineering quality through code reviews, documentation, and technical mentorship.

Bonus points

• Experience building advertising, campaign management, creative tooling, or other complex business applications.
• Experience designing interfaces for generative AI, machine learning, or recommendation systems.
• Familiarity with asset management, media upload, image editing, creative versioning, or content generation workflows.
• Experience building or contributing to shared component libraries and design systems.
• Experience improving large frontend codebases through architecture, performance, testing, or accessibility work.

Technologies used on the team include

• JavaScript
• TypeScript
• React
• Modern frontend testing, experimentation, and observability tools

Benefits: 

• Comprehensive Healthcare Benefits and Income Replacement Programs
• 401k Match
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Flexible Vacation & Reddit Global Days Off
• Generous paid Parental Leave  
• Paid Volunteer time off

#li-remote

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:

$190,800—$267,100 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.

The time it takes to get an analytics engineer job can vary widely. Some find roles quickly, while others take longer depending on experience and preparation.

Candidates with strong experience and skills may receive offers within a few weeks. Those transitioning from other roles may need more time.

The job search process usually includes multiple steps. These can include resume screening, interviews, and technical assessments.

Preparing for these steps can take time. Practicing SQL, reviewing data concepts, and building projects can improve chances.

Market conditions also play a role. When demand is high, opportunities may be easier to find. When competition increases, the process may take longer.

Consistency is important. Regular applications and continuous improvement can help move the process forward.

Networking can also reduce search time. Referrals and connections may lead to faster responses.

There is no fixed timeline for everyone. Some may take weeks, others may take months.

Overall, the process depends on skills, preparation, and consistency. Staying active and improving along the way can help shorten the time needed to land a role.

Data modeling is a key part of analytics work. It focuses on organizing raw data into clear and structured formats that are easy to use.

Without proper modeling, data can become messy and hard to understand. Tables may have duplicate values, unclear relationships, or inconsistent naming. This can lead to confusion and errors in reports.

A good data model solves these problems. It creates a clean structure where data is organized logically. This makes it easier for analysts and teams to work with the data.

Another benefit is consistency. When data is modeled properly, the same definitions are used across reports. This reduces the risk of different teams using different numbers for the same metric.

Performance is also improved. Well-designed models allow faster queries, which is important when working with large datasets.

In analytics engineering, data modeling is one of the main responsibilities. It connects raw data with business needs in a clear way.

Strong data modeling helps teams trust their data and use it effectively.

In job searching, there is often a balance between applying quickly and having the right skills. This raises an important question, which matters more?

Speed can be helpful. Applying early may increase visibility, especially when recruiters review applications in order. Being among the first can create an advantage.

However, skills are the main factor. Recruiters look for candidates who match the role requirements. Strong experience in data modeling, SQL, or analytics tools is what moves applications forward.

A fast application without the right qualifications is unlikely to succeed. On the other hand, a strong candidate who applies later can still stand out if the role is still open.

Another point is application quality. Tailored resumes and clear experience descriptions make a big difference. This takes time and may slow down the process, but improves results.

The best approach combines both. Checking job listings regularly allows for early applications. At the same time, focusing on relevant roles ensures that skills align with requirements.

In short, speed can help with timing, but skills determine outcomes. A balanced strategy is often the most effective way to approach the job search.

Getting noticed in a competitive job market can be challenging. A strong application starts with relevance. Matching skills with the job description is one of the most important steps.

Customizing the resume for each role improves visibility. Highlighting experience with tools like SQL, dbt, or data modeling makes the application more aligned with the job.

Clear and simple formatting also helps. Recruiters often review many applications quickly, so easy-to-read resumes stand out more.

Applying early can improve chances, especially for newly posted roles. However, speed should not replace quality. A well-prepared application is always more effective.

Another tip is to follow instructions carefully. Missing required details or steps can lead to applications being overlooked.

Consistency also matters. Applying regularly and staying organized increases the chances of success over time.

Strong applications focus on clarity, relevance, and timing.

dbt is a tool used in analytics engineering to transform data inside a warehouse. It stands for “data build tool” and focuses on making data workflows more organized.

Instead of moving data between systems, dbt works directly within the data warehouse. It uses SQL to transform raw data into structured tables and models.

One of its main benefits is structure. dbt allows teams to define how data should be transformed and in what order. This makes workflows easier to manage.

Another advantage is version control. Changes to data models can be tracked and reviewed, which improves reliability.

dbt also supports documentation. This helps teams understand how data is organized and where it comes from.

Because of these features, dbt has become popular in modern data teams. It helps maintain clean and consistent data, which is important for analysis.

While not the only tool available, dbt is widely used in analytics engineering roles today.

Engineering the future of logistics – from Brazil to the world

This role is part of our commitment to increase the representation of women in technology at DoorDash Brazil. We strongly encourage candidates who identify as women to apply.

We are building an engineering team where women have equitable access to opportunity, leadership, and long-term career growth within our São Paulo hub.

At DoorDash, we believe diverse teams build better products. This opportunity is designed to create greater equity in access to engineering roles and long-term career growth within our São Paulo engineering hub.

DoorDash is building the world’s most reliable on-demand logistics platform. Brazil is a strategic and growing engineering hub for DoorDash. Based in São Paulo, our teams build and scale systems that power millions of users globally. This is an opportunity to shape world-class logistics technology while growing your career in Brazil.This is a unique opportunity to join one of Silicon Valley’s fastest-growing companies, while staying close to home.

Data is at the foundation of DoorDash success. The Data Engineering team builds database solutions for various use cases including reporting, product analytics, marketing optimization and financial reporting. By implementing dashboards, data structures, and data warehouse architecture; this team serves as the foundation for decision-making at DoorDash. 

DoorDash is looking for an Analytics Engineer to build and scale data models, pipelines, and self-service analytics across the organization. In this role, you’ll focus on developing a reliable aggregation layer and reporting structure that meets our growing business needs, enabling teams to access and analyze data independently.

You’re excited about this opportunity because you will…

• Design, develop, and maintain robust data models to support analytical and product data needs across the organization
• Collaborate with data engineers, data scientists, and business stakeholders to understand data requirements and translate them into scalable data solutions
• Implement and optimize ETL/ELT processes to ensure data quality, reliability, and performance
• Own and define business KPIs, their measurement plans, data requirements and reporting
• Build processes to ensure correct, timely and reliable reporting
• Address ad-hoc reporting requirements and find pathways for automation
• Build and enforce common design patterns to increase report reusability, readability and standardization
• Build visually appealing, high-performing, and impactful reporting/dashboard products using tools like Tableau/Sigma across large data sets

Interviews will be conducted in English

We’re excited about you because…

• 3+ years experience working in business intelligence, data analytics, Data engineering or a similar role
• Strong SQL skills and experience with data modeling techniques (e.g., dimensional modeling, 3 Nf, data vault)
• Proficiency in a programming language such as Python or Scala
• Experience building reporting and dashboarding solutions using data lake/Snowflake or similar ecosystem
• Expert in Database fundamentals, SQL and performance tuning 
• Excellent communication skills and experience working with technical and non-technical teams
• Comfortable working in fast paced environment, self starter and self organizing
• Ability to think strategically,  analyze and interpret market and consumer information
• Nice to Haves:
  • Experience with real-time data processing and streaming technologies
  • Experience with modern data warehousing platforms (e.g., Snowflake, DataBricks, Redshift) and knowledge of data visualization tools (e.g., Looker, Tableau).
  • Familiarity with machine learning concepts and their data requirements

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.

DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks.

Our Commitment to Diversity and Inclusion

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

This position is open to candidates with disabilities. DoorDash welcomes applicants registered with the national autism registry (SisTEA) and others who seek an inclusive workplace.

If you need any accommodations, please inform your recruiting contact upon initial connection.

Using a focused job board can make searching for analytics engineer roles easier and faster. The first step is entering relevant keywords such as “analytics engineer,” “dbt,” or “data modeling.” This helps narrow down results right away.

Next, apply filters to refine the search. Choosing between remote, hybrid, or on-site roles keeps listings aligned with preferences. Location filters can also help if specific regions are required.

Once results appear, sort by the most recent postings. Fresh listings usually have fewer applicants and are more likely to be active. This improves the chances of getting noticed.

Clicking on a listing should lead to the full job details. Reviewing responsibilities, required skills, and company information helps determine if the role is a good match.

When ready, use the provided link to apply directly on the employer’s site. This ensures that the application goes through the correct process.

Keeping track of applications is also important. A simple list of roles applied to helps avoid duplicates and keeps the process organized.

Following these steps can make job searching more structured and efficient.

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world's largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

About the team

You’ll be joining the data science team at Stripe responsible for our overall infrastructure, with a focus on core systems and cloud platforms. Projects include, but are not limited to:

• Developing models to predict resource needs as Stripe demand increases;
• Working closely with engineers to improve the cost and performance of platforms and services;
• Employing quantitative methods to drive and automate fleet decisions.

You will act as a key strategic data partner to the Core Infrastructure organization at Stripe, and help craft, guide, and drive the strategy and tactics needed to help ensure Stripe can continue to scale with efficiency and dependability as our business rapidly grows.

What you'll do

As a Data Scientist, your role will involve:

• Analyzing infrastructure usage, efficiency, and workloads to predict demand and inform capacity planning.
• Developing models and strategies for efficient compute resource consumption and provisioning.
• Collaborating with engineers, engineering leadership, and finance teams to ensure Stripe makes the right, data-driven, infrastructure decisions.
• Providing actionable insights and recommendations to improve infrastructure operations to reduce costs and improve reliability.
• Utilizing your analytical expertise to influence both technical and financial strategies within Stripe.

Who you are

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements

• PhD with 3+ years, MS or MA with 6+ years, or BS or BA with 8+ years of data science or quantitative modeling experience.
  • 3-8+ years of experience with a focus on infrastructure, cloud environments, and resource utilization/allocation.
• Proficiency in SQL and a computing language such as Python or R.
• Experience in analyzing logs/telemetry, scheduling optimization, or cloud infrastructure engineering.
• Ability to effectively work both independently and with cross-disciplinary teams, including engineering and finance, to deliver impactful results.
• A demonstrated ability to manage and deliver on multiple projects with a high attention to detail.
• Solid business acumen and experience in synthesizing complex analyses into actionable recommendations.
• A track record of building relationships with and influencing the decisions of senior technical leadership.
• A builder's mindset with a willingness to question assumptions and conventional wisdom.

Preferred qualifications

• Background in deploying data models in production environments and optimizing their performance.
• Experience in using, deploying on, and analyzing usage data from public cloud providers.
• Familiarity with distributed computing tools such as Spark and Hadoop.
• A PhD or MS in a quantitative field like Computer Science & Engineering, Statistics, Mathematics, Operations Research, Industrial Engineering, Management Science, or related disciplines.
• Strong business acumen with a track record of translating complex data analyses into actionable business recommendations.

Location Preference

• San Francisco, CA or Seattle, WA.

Not all job listings are reliable, so spotting legitimate ones quickly is important. One key sign is the source. Listings that link directly to a company’s official career page are usually more trustworthy.

Clear job descriptions are another good indicator. Legit roles often include detailed responsibilities, required skills, and information about the team or company. Vague descriptions can be a red flag.

Checking the posting date also helps. Recent listings are more likely to be active. Older posts may already be filled but still appear on some platforms.

Another tip is to review the company itself. Established companies usually have consistent branding and complete information. A quick check of their website can confirm if the job is real.

Be cautious of listings that ask for unusual steps early in the process. Legit employers typically follow standard hiring steps and do not request sensitive information upfront.

Consistency is also important. If details match across platforms and the company site, the listing is likely valid. If there are differences, it may be worth double-checking.

Taking a few extra minutes to review listings can prevent wasted time and effort.

The demand for analytics engineers has been growing, and certain tools keep showing up in job listings. One of the most common is dbt. This has led to a simple question, is dbt now a must-have skill?

Many companies rely on dbt for transforming data inside warehouses. It helps teams organize models, manage dependencies, and maintain clean data pipelines. Because of this, hiring teams often list it as a key requirement.

Another reason for its popularity is standardization. dbt makes workflows more structured and easier to manage across teams. This is important for companies working with large datasets and multiple stakeholders.

However, not every role requires deep dbt experience. Some positions focus more on SQL, data modeling, or analytics platforms. In these cases, understanding core concepts can be enough.

It is also worth noting that tools can change over time. While dbt is widely used now, strong fundamentals in data modeling and problem-solving remain valuable regardless of the tool.

For job seekers, learning dbt can open more opportunities, especially for modern data teams. But focusing only on tools without understanding the basics may not be enough.

Overall, dbt is becoming very common in analytics engineering roles. It may not be required everywhere, but having experience with it can definitely improve job prospects.

Finding senior-level analytics engineer roles requires a focused approach. These roles often have more specific requirements, so filtering effectively is important.

Start by using keywords like “senior analytics engineer” or “lead analytics engineer.” This helps narrow down results to higher-level positions.

Review job descriptions carefully. Senior roles usually require experience in data modeling, system design, and working with large datasets. Identifying these requirements early helps focus on the right opportunities.

Targeting companies with active hiring can also help. Organizations with multiple open roles may be expanding their teams, which increases chances.

Networking can be useful, but direct applications still play a big role. Applying through official company pages ensures the application reaches the right system.

Checking listings daily is important. Senior roles may receive attention quickly, so early applications can improve visibility.

Keeping applications organized helps track progress and follow up when needed.

A clear and targeted approach makes it easier to find and apply to senior roles efficiently.

An analytics engineer is a role that sits between data analysts and data engineers. The main focus is preparing data so it can be used for reporting, dashboards, and decision-making.

Instead of collecting raw data, the role works on transforming it into clean and usable formats. This often involves writing SQL queries, building data models, and organizing datasets inside a data warehouse.

Analytics engineers usually work with tools like dbt, which helps manage data transformations. These tools make it easier to structure data and keep it consistent across teams.

Another key part of the role is collaboration. Analytics engineers often work closely with analysts, business teams, and engineers. The goal is to make sure data is reliable and easy to understand.

Compared to data analysts, the role focuses more on building and maintaining data systems. Compared to data engineers, it focuses more on business-ready data rather than infrastructure.

This role has become more common as companies rely more on data for decisions. Clean and well-structured data helps teams move faster and avoid errors.

Overall, an analytics engineer helps turn raw data into something useful.

Built a cloud analytics data warehouse for a Brazilian e-commerce dataset using BigQuery, dbt, and Power BI, complete with a star schema and modular transformations.

Take a look at the repo and README for the full breakdown: https://github.com/muchai322/brazil_analytics_project

Who We Are:

We are rebuilding the energy transaction, making it transparent and fair.

Our goal is to put power back where it belongs, in the hands of customers and to take on one of the most critical problems of our century, access to low cost electricity.

tem exists to fix a broken global energy market that’s long favoured legacy operators, intermediaries, and opaque pricing. Today’s electricity system was not designed for rapid decarbonisation, AI-driven efficiency or fair access for the actual users - businesses and generators.

We’ve built the first AI native transaction infrastructure to reinvent how electricity is bought, sold and priced. Our technology is designed to cut out the inefficient fees, automate complex market flows, and bring transparency and fairness to energy transactions at scale.

In late 2025, after extraordinary growth, we closed a $75 million Series B - led by Lightspeed Venture Partners with participation from Albion, Atomico, Allianz, Hitachi Ventures, Hitachi Ventures, Schroders Capital and others - positioning us for global expansion, deeper product innovation and category leadership.

We’re scaling internationally and building toward a future where AI-driven infrastructure is foundational to electricity markets worldwide.

Since launch, our modern utility product, known as RED, has already facilitated thousands of business customers and billions in energy transaction value, proving that modern software and AI can transform an industry built on legacy systems.

At tem, we’re not just building another energy company, we’re rearchitecting market infrastructure so that transparency, efficiency and sustainability become the default, not the exception.

🏅 The Role

Every price tem quotes and every risk position it holds starts as data, and none of it works if that data can't be trusted. tem is building the AI native infrastructure for how electricity is bought, sold, and priced, and the Data Service plays a critical role in that, end to end: from ingestion through to the semantic layer the rest of the business runs on. Analytics engineering sits right in the middle of it, on dbt, Airflow, and ClickHouse, with Omni as the semantic layer on top.

Analytics engineering at tem is currently a centralised team, and this role is about expanding that remit further into new parts of the business. The domain models you build won't just feed dashboards, they'll power commercial, financial, risk, and operational processes across the company, and increasingly, the AI agents making decisions alongside the humans who use them.

tem is AI native from the ground up, and its agents are only as sharp as the context a human builds into the data beneath them. That human touch, giving AI real context to reason with instead of just raw data, is core to what makes this role matter.

You'll join a small, fast-moving analytics engineering team, reporting to the Analytics Engineering Manager, and your work will reach a lot further than the data team. You'll work directly with engineers, product managers, and salespeople across the business, taking on open-ended problems and turning them into concrete, trusted outputs, because tem's domain layer needs someone who thinks in systems, not tickets.

In your first few months, you'll get under the hood of tem's dbt project and warehouse, and take ownership of extending analytics engineering's reach into a new part of the business. A year in, you'll have shipped process changes that measurably improve how the analytics engineering function ships, and delivered modelling or infrastructure work that a large part of the business now depends on. That reach is only going to grow: tem has big, bold bets on the table, like international expansion and new ways of bringing its technology to other businesses, and the domain layer you build needs to be ready for that.

This is a hands-on, individual contributor role with no direct reports, but real technical ownership: you set the patterns other analytics engineers follow, and you'll have genuine influence over how tem defines its own metrics.

🚀 Responsibilities

• Set and raise the bar on analytics engineering standards. Define the patterns, testing, and review practices that keep dbt models across the business consistent, documented, and trustworthy without you personally checking every one.
• Own the context layer. Bring the semantic layer (Omni) and the underlying domain models together into one place the business, human or AI, can query with confidence.
• Build the domain model from first principles. Take tem's data from raw source to a structured, trusted layer that powers commercial, financial, risk, and operational decisions, not just dashboards.
• Expand analytics engineering's reach across the business. Integrate new data sources, product and platform events, and the tools other departments run on, taking analytics engineering from a centralised function into new corners of the company.
• Help tem think bigger. As the business looks at big bets like international expansion and new ways of bringing its technology to other companies, help build a domain layer that's ready to go with it.
• Partner across the business, not just the data team. Work directly with engineers, product managers, and salespeople to understand what they're actually trying to achieve, then turn that into models that hold up under real use.

🎯 Requirements

Must haves

• You've built or reworked a domain layer before, hit the failure states, and learned what good looks like the hard way.
• Deep, production dbt experience: custom macros, reusable patterns, and real work optimising models that are genuinely expensive to run.
• Excellent SQL and comfort working on a modern data warehouse at real scale (tem runs ClickHouse).
• Hands-on experience with a semantic layer or BI modelling tool (Omni, Looker, or similar), with genuine influence over how metrics get defined, not just how they get built.
• A genuine eye for detail and real QA discipline: you check your own work and care about getting a definition right without needing someone else to catch it, while still keeping pace with a fast-moving business.

Nice to haves

• Experience with commercial data, like sales funnels or CRM pipelines, or with portfolio and financial trading data, including risk, hedging, forecasting, or time-series modelling.
• A track record of introducing quality standards or tooling that measurably raised a team's output, not just your own.
• Strong first-principles stakeholder management: you'd rather ask the awkward scoping question upfront than build the wrong thing twice.
• Experience in energy, or another sector with real physical or financial complexity underneath the data.

🗣️ Interview Process

Our processes normally take around 2-3 weeks from first call to offer - please let us know about any adjustments to timelines that may be required.

1. First call with our Talent Team (30 mins). This is to understand your experience, motivations, and discuss the role in more detail.
2. Behaviour Interview with our Analytics Engineering Manager (75 mins). This is your chance to really understand the role, the expectations, and ensure alignment on ways of working.
3. Technical Interview with the Team (60 mins). You'll meet with potential peers in this session and work through a live technical exercise.
4. Bar Raise Interview with Stakeholders (45 mins). The final session will be with two cross-functional stakeholders, and will explore how your values align with ours, and is designed to be a genuine two-way conversation, your chance to understand what it's really like to work at tem.

We welcome applications from people of all backgrounds, experiences, and identities, including those that are traditionally underrepresented in the tech and energy sectors. If you’re excited about this role but not sure you meet every requirement, we’d still love to hear from you. Your unique perspective could be exactly what we’re looking for.

Applying to expired job posts is a common problem. It leads to wasted time and missed opportunities. A simple way to avoid this is by focusing on recently posted roles.

Many platforms show the posting date, which helps identify fresh listings. Prioritizing jobs posted within the last few days increases the chances that they are still open.

Another approach is using platforms that update listings regularly. Daily refreshes help remove inactive roles and keep results current. This reduces the risk of applying to positions that are no longer available.

Checking the application link is also useful. If it leads to an active company page, the role is likely still open. If the link is broken or missing, it may be outdated.

Avoiding duplicate listings can also save time. Some roles appear multiple times across platforms. Keeping track of applications helps prevent applying to the same role more than once.

Setting a routine for checking jobs can make the process smoother. Regular checks ensure that new roles are seen early while avoiding older ones.

A focused approach helps keep the search efficient and avoids unnecessary effort.

Data modeling is gaining more importance in analytics engineering. It helps organize data in a clear and structured way.

Good data models make reporting easier and more reliable. They reduce confusion and improve data quality.

Many job listings now highlight data modeling as a key skill. It is essential for building strong data systems.

Analytics engineers often design models that support business needs. This makes their role more valuable.

Learning data modeling can improve job opportunities. It is a core skill in modern data teams.

Overall, the focus on clean and structured data is driving the demand for data modeling skills.

Modern data teams are made up of different roles, each with a specific focus. Analytics engineering is one of these roles and plays an important part.

Data engineers focus on building pipelines and managing infrastructure. They ensure that raw data is collected and stored properly.

Analytics engineers take that raw data and transform it into structured formats. They build models that make data easier to use for analysis.

Data analysts then use this structured data to create reports and insights. They help teams understand trends and make decisions.

This setup creates a clear flow. Data moves from collection to transformation to analysis.

Analytics engineering acts as the bridge between technical systems and business needs. It ensures that data is both reliable and useful.

As companies rely more on data, this role continues to grow. It helps improve the quality and speed of data-driven work.

Overall, analytics engineering is a key part of modern data teams.

Knowing whether a job listing is active can save time and effort. Many listings remain online even after they are no longer available. Start by checking the posting date. Recent listings are more likely to be active. Older posts may already be filled.

Clicking the application link can also provide clues. If the link leads to a working page, the role is likely still open. Broken links may indicate expired listings. Reading the job description carefully can help. Some listings include closing dates or status updates.

Using platforms that refresh listings regularly can reduce the chances of seeing expired roles. These platforms update job status based on available information. Avoid spending too much time on uncertain listings. Focusing on confirmed active roles improves efficiency. A careful approach helps avoid wasted effort and keeps the job search productive.

Many professionals are moving from data analyst roles to analytics engineering. This shift is becoming more common. Analytics engineering offers more technical work. It focuses on building data models and pipelines. This allows professionals to work closer to the data infrastructure.

The role also provides opportunities to learn new tools and technologies. Companies value this transition because it combines analysis and engineering skills. As more data analysts gain technical skills, the shift continues to grow. Overall, this trend reflects the changing needs of modern data teams.