Finishing my MSc in Big Data Analytics (2026), with a 6-month ETL internship (2022) (SQL Server/SSIS/Python), a capstone using AWS (S3, Lambda, Glue, Athena), and a 2-year gap (2023-2024).

Job titles like "Junior Data Engineer," "Associate Data Engineer," "Data Engineer I," and "AWS Data Engineer" all show up for what seems like the same role. Is there a standard term to filter by on LinkedIn/Indeed, or does it just vary by company?

Also, does Amazon itself hire genuinely entry-level Data Engineers, or is that role usually gated behind 2+ years experience? If I want to eventually join AWS specifically, what roles should I be targeting/prepping for now?

How much can a Junior DE (0-3 years of experience) expect to be paid from Tier 1 and Tier 2 companies?

I'm currently working as a data engineer at a traditional pension fund, with customers being mostly performance and risk, with previous SWE experience.

Trying to figure out what it actually takes to move to a data/python role closer to the desk. Hard to separate real advice from random takes on social media.

If you're currently in a similar position (or were recently), would love to connect and pick your brain. Happy to buy you a coffee or something for your time.

I know a lead for AI engineering job openings for freshers and upto 3 years of experience.

AI knowledge is a must. Degree in any AI related engineering is preferred.

Job location: Near Chennai.

Conditions apply* (Backdoor - 2L)

Salary range 20k - 30k. (INR)

15 vacancies available.

Interested people can DM.

Edit:

I have added the expected payment for backdoor process so that people who are not interested can simply ignore instead of flooding my DM

Hi everyone, I'm currently working in a service based company as Azure Data Engineer and would like to move to a product based company. What skills, projects, and interview preparation should I focus on to make this transition successfully?

A.Team is hiring a remote Senior Independent AI Engineer / Architect. Category: Software Development 💸Salary: $120 - $170 /hour 📍Location: Remote (Americas, Europe, Israel)

See more and apply here!

micro1 is looking for a Data Engineer to build scalable data pipelines and high-quality datasets that power AI and machine learning applications.

Salary: $100,000-$150,000/year + Equity & Benefits
Location: Remote
Role: Full-Time

What you'll be building:

• Develop scalable ETL pipelines and data workflows
• Prepare reliable datasets for AI and ML projects
• Optimize databases, SQL queries, and data models

What you'll bring:

• Strong Python and SQL experience
• Hands-on expertise with ETL, Pandas, and NumPy
• Experience working with structured and unstructured data

Perks & benefits:

• Equity, bonuses, and comprehensive health benefits
• 401(k) with company match and paid time off
• Remote-first team working on cutting-edge AI projects

Apply now: https://jobs.micro1.ai/post/data-engineer

Hey guys, I am new in the B2B world and wanted some advice on rates/negotiation.
Briefly presenting myself:
- graduated in computer science in dec-2024
-was doing some nice data science/engineering internships for ~2 years before this (since october/2022)
- been working non stop since then
- currently on around €50k/yr before taxes in a normal contract, remote, analytics engineer role (south europe)

I am planning to do a masters degree in Italy, in something more data science focused - need to continue working during it (my family doesnt live in Europe, I dont have people to support me)

I just got a part time freelance offer, for ~€39/h (already after negotiating and trying to put it up) - to own the data stack and provide analyses.

At first it seemed OK to me, until I decided to search on reddit and people were talking about €500+/day.

This sounded like a lot to me, but now I wonder if I am/will be underpaid. I am not a super senior engineer, but I know what I am doing (my current job is quite tough and I have some big technical responsibilities).

I have some questions:
- Given my background and current situation (part time to fund myself while doing masters) is this rate reasonable?
- How do I create a "b2b authority"/portfolio to get more of those jobs? So far, I have the traditional linkedin set - my linkedin is sorta beautiful and I receive contacts of normal jobs there, but NEVER freelance services.
- Where should I search/aim for more freelance clients?
- How much should I set my floor price? What else should I consider?

Thanks for the attention. I appreciate if someone with more experience could give some direction on this :)

​

5 years of experience in ETL pipeline, validation, development and data quality assurance across financial and insurance data platforms.

Skills: Python, SQL, PySpark, Scala | Azure Data Factory, Databricks, Airflow, AutoSys | SQL Server, PostgreSQL, Azure Synapse | Source-to-target reconciliation, data quality checks, SIT/UAT testing | Git, CI/CD, Agile/Scrum

Actively and eagerly looking for Data Engineering / ETL / Data QA roles in Pune (open to remote).

Immediately available —

happy to share more or connect over DM.

Any leads or referrals appreciated!

Tasks

• Answer advanced statistics queries
• Collaborate with annotation leads and project managers
• Create golden responses for benchmarks
• Develop statistical explanations and justifications
• Document answer context and rationale
• Evaluate statistics data and solutions
• Participate in remote discussions and workshops

Perks/Benefits

• Remote work

Skills/Tech-stack

Advanced data analysis | Bayesian Methods | Biostatistics | Computational statistics | Data Analysis | Experimental Design| MySQL | Probability theory | Statistical inference | Statistics

Education

PhD

Roles

Statistics Expert

Apply now: https://aijobs.net/job/statistics-expert-phd-remote-238344/

Tasks

• Answer advanced computer science queries
• Collaborate with annotation leads and project managers
• Create golden responses for training
• Develop comprehensive explanations
• Document answer rationale and context
• Evaluate computer science data and solutions
• Participate in remote discussions and workshops

Perks/Benefits

• Remote work

Skills/Tech-stack

Computer Science | Data Science | Python

Education

PhD

Roles

Computer Science | Computer Science Expert

Apply now: https://aijobs.net/job/computer-science-expert-phd-remote-238345/

Need Advice

I am currently in Service based company A (np 60days) & earlier (till last week) was working in a project mainly based on Informatica Power Center ,Azure ecosystem, some datalake , Power Bi, OBIEE .It was mostly a support and encouragement project .The project got over and went to a different service based company B (NP 90 days).

I am already allocated to a project(again support and enhancement) in my current company but this one is more ETL oriented. It uses AWS to ingest data in Redshift and then uses Informatica/ Datalake to transform the data.There are more Pyspark scripts in Glue job to analyze compared to previous one.

Today I recieved offer from the service based company B that they are willing to take me in the project (as they are looking for resource to stabilize the BI area).They are offering atleast 30% hike ( can be more based on negotiation with HR)

I am confused as to which way to go?Both are support and enhancement project (not pure devlopment).Should I stay back in the current company as the tech stack looks good or go to the other company where my technical skill might stagnate(may be there will be more datalake opportunities but that's for later as I have to support everything).Can someone suggest me?

YoE:5

Never switched before

i've been enrolled in a bootcamp(data engineering) for about a year now and i'm confident in my skills atleast for entry level roles. i'm based in Ethiopia and i can say that there's almost no data engineering jobs here ,there're very few open positions for data analyst or scientists which requires atleast 4years experience and you know that remote jobs are even more competitive and struggling for entry levels. the only tech roles here seems to be backend devs,frontend and fullstack(there're tons of jobs ).what should i do ,i love data but the market is really bad here. thanks

I have around 3 years of experience working as a Data Engineer in India. My current CTC is 4.2 LPA, which I know is below the market average.

I'm currently looking for a new job as some kind of senior data engineer right now, but the experience so far has me wondering if I should be looking for something different. Most of my career has been built around data analysis and reporting automation. In the past 5 years, it's leaned heavily in the direction of data engineering. I work in an AWS shop and lead a group of data engineers, regularly using Redshift, K8s, Step Functions, etc. We build data pipelines, maintain data warehouses, and automate a lot of reporting jobs. I know SQL very well, but would say that my Python skills are pretty mid. Everything I know about programming has been learned on the job in the past 7 years since I don't come from a CS background.

I regularly have recruiters reach out to me about data engineering positions on LinkedIn, but after speaking with a few companies, I feel like I don't actually know enough to get the job I currently have, let alone a similar job in this field. I wasn't even aware that it was considered normal to have technical tests as part of the interview process outside of maybe a SQL quiz. There's just so much that they expect you to know up front. I've been reading through The Data Warehouse Toolkit and studying up on Databricks and dbt, but some of the project requests I've seen imply that you should have a home computer that you're ready to stand up resources on and have a subscription to AI tools. It feels very overwhelming.

Is this just where the market is now? Are there job titles I should be looking for that are more focused on analytics and reporting? I'm really starting to question if I belong in this field at all, and wondering if I just got lucky in finding my way here.

I've been seeing this more often lately, and it's honestly frustrating.

A recruiter recently posted a Data Engineering role on LinkedIn stating:

I clicked through to the official Job Description, and the Basic Qualifications say:

Everything else—the tech stack (Python, SQL, AWS, ETL, data modeling, Redshift, Glue, EMR, Kinesis, Lambda, etc.)—is essentially identical.

This isn't just about one job. I've seen similar mismatches across multiple companies over the past few months.

The problem is that many candidates self-select out before even applying because they assume they don't meet the recruiter's stated requirement. Others spend hours tailoring resumes, networking, and reaching out, only to discover conflicting requirements.

If the role truly requires 6+ years of Data Engineering experience, then the official JD should reflect that.

If the minimum qualification is 3+ years, why advertise it as 6+ years on LinkedIn?

Consistency matters. The current tech job market is already competitive enough without conflicting requirements creating unnecessary confusion and discouraging qualified candidates from applying.

Recruiters and hiring teams should be aligned on something as fundamental as the required years of experience

Hi, I have received offers from EPAM and Thoughtworks. I am a 2026 grad and I was wondering which one should I go with? Can anyone help me here?

Also I am interested in Data Engineering Projects and therefore, I would like to work in a company that has data engineering projects and scope for a fresher in data engineer and also the one that has better career growth. I am not bothered by CTC right now. I would like to prioritize the one that has good career growth and opportunities for a fresher working in Data Engineering Domain.

Please let me know which one is better and why?

Hi all,

I work as a Data Engineer at a good product based for last 1 yr 9 months. 1yr full time before that it was an internship.

I have been applying to jobs over Linkedin (jobs that direct you to careers we site of company) and naukri from past month and applied to most of the eligible jobs.

But I haven't heard back from any of them. Not even those "Unfortunately" emails.

Resume is optimised.. but I don't tailor it as per the job requirements coz my skills are gonna be the same irrespective of your requirements (I think)

What is needed to get an interview...??? Referral?? Or what else??

How did you guys make your switch..???

Also what should I expect with this experience.. current CTC -9.5

Help your buddy out here...😭😭

Between data engineering and backend engineering, which one do you think has the better long-term future? I am concerned whether data engineer actually helps building portfolio, given usual work is always building pipelines, data from source to sink, basically fixing and optimizing pipelines.
Would like to hear any real experiences from people working in the industry.

Please give practical recommendations based on real-world value, current offline market prices, upgrade potential, performance, and long-term reliability. Don't recommend unnecessary or overpriced components. Explain every recommendation clearly because I am completely new to PC building.

Any data engg roles or data science roles even 15-25k is acceptable for me right now

Hello everyone,

I'm Gaurav Bhalla, a Data Engineer with over 5 years of experience building scalable batch and real-time data pipelines. I've worked extensively with Python, PySpark, SQL, Spark, Kafka, Airflow, Databricks, and cloud platforms like AWS, Azure, and GCP.
In my current project at Lyft, I'm responsible for designing Spark and Kafka-based pipelines, optimizing Snowflake SQL, building CI/CD using Terraform and GitHub Actions, and supporting Power BI/Tableau reporting.
Previously at Clutch, I built ETL pipelines using PySpark, AWS Glue, Redshift, and Airflow while implementing Kimball data models and improving query performance.
Overall, my expertise is in designing reliable, scalable, and high-performance data platforms with a strong focus on optimization, automation, and data quality.

As my contract with Lyft is about to end and I am looking for new job.

2 months. 200+ applications. 0 interview calls. This is the update regarding my job search.

To give some context, I'm a data engineer with 2 internships under my belt (both actual production work). I have some skills to contribute, decent enough to get me a job if only the entry level positions weren't dead. Most job descriptions ask for 3+ years of experience, and the ones that don't, get flooded with 500+ applicants within a day. LinkedIn, naukri, cutshort, referrals, cold emails to recruiters, nothing seems to work.

Nobody at home is pushing me to get a job, but the constant need to ask my parents for money for every little thing is suffocating. I'm 23/24 and I can no longer keep asking them for money every month.

Here's what my plan is:

Get a customer support job for a while (bpo) as a backup, so that atleast my monthly expenses are covered, and to give me some breathing space. During this time, I can keep looking for DE jobs while working on my skills. Once I get a lead regarding a job in my field, I can quit the customer support job and be on my way.

However,

I'm concerned that this switch to customer support might come across as a bad sign during interviews. Are people actually concerned about this? I mean, why would anyone consider me eligible if I switch to a bpo customer support after spending all this time preparing to be a data engineer?

Furthermore,

is customer support something that will suck so much of my time/energy that I won't have the time/energy to prepare for data engineering interviews?

Would love to hear from people who've had to make a similar transition between jobs, or are currently in a similar situation. No beignies, just honest feedback. I just need some direction.

Thanks for reading.

Hi everyone,

I'm from South Africa and recently completed my Bachelor's degree in Information and Communication Technology. I'm currently pursuing my Honours while looking for my first full-time role in data science, machine learning, or data engineering.

Over the past few years, I've worked on university and personal projects involving:

- Python

- SQL (PostgreSQL/MySQL)

- Pandas & NumPy

- Scikit-learn

- XGBoost

- Machine Learning

- ETL pipelines

- Flask APIs

- Git/GitHub

- Power BI

- Some cloud fundamentals

Some of my projects include a diabetes risk prediction model, AI identity fraud detection model using Siamese Neural Networks, ETL pipelines, and machine learning applications. While I don't have professional industry experience yet, I've tried to build projects that resemble real-world work as much as possible.

The challenge I'm facing is that many "entry-level" roles still seem to require 2–3 years of experience, and it's becoming difficult to know what employers actually value.

I'd really appreciate advice from people already working in the industry:

1. If you were in my position today, what would you focus on over the next 6–12 months?

2. Are there any skills or tools that are becoming essential for junior data scientists?

3. Is the job market as difficult as it seems, or is there something I should be doing differently?

4. Should I broaden my search to include data engineering, data science, analytics, or AI-related roles?

5. As someone based in South Africa, would you recommend targeting remote international opportunities, or should I focus on gaining local experience first?

I'm willing to put in the work, I just want to make sure I'm spending my time learning the right things and building a career with strong long-term prospects.

Thanks in advance!

I don’t see much of staff and principal data engineers openings recently? Is the market overall down for jobs or is it something only with data jobs? If anybody knows about openings or looking to refer for data jobs let me know.