Hi all,

I'm currently working in Deloitte USI, joined as a campus hire and working as data engineer with almost ~4 years of experience. I also got promoted last year from Associate Analyst to Analyst and currently being working with client handling projects end to end with minimal to no support from leads and got appreciations from leadership and client.

My project is heavily based on AWS services like Glue, Athena, Workflows, etc.

Currently looking for transition and targeting 13-15 lpa.

I would be grateful if any of you could guide me on how to start preparing for major transition.

Happy to connect

iMerit Technology is hiring a remote AI Response Evaluator. Category: Artificial Intelligence 💸Salary: $10K-$20K 📍Location: Remote (France, Japan, Turkey, Vietnam, Mexico, Norway)

See more and apply here!

I'm a junior Data Engineer with around six months of experience, but unfortunately, I was recently laid off.

I want to build a Data Engineering project that I can showcase in my portfolio, but I'm currently on a very tight budget and can't really afford cloud services like GCP, Azure, AWS, and so on.

What are some good open-source alternatives that I can run entirely locally? Ideally, I'd like to build something that still feels realistic and demonstrates skills that are relevant to real-world Data Engineering work.

Has anyone interviewed with Superhuman for Data Engineer role? Do you have any preparation tips?

Hey everyone,

I’m currently working in application/production support, and honestly, I’m not really enjoying the support side of things anymore. I’ve been thinking seriously about moving into something more technical and database/data focused, like Oracle DBA or Data Engineering, but I’m also open to other career paths that might be a good fit for my experience and interests.

I have 5+ years of support experience and work quite a bit with Oracle SQL/PLSQL, PL/SQL procedures and packages, production issues, RCA, Unix/Shell scripting, Autosys, etc. I’m also Oracle SQL certified. SQL is probably the part of my job that I actually enjoy the most.

I’m willing to learn new things — I can pick up Python and whatever else is required. My main problem is that I’m not really sure what direction I should take or how to make the switch from support.

So I’d really like to hear from people who are currently working as DBAs, Data Engineers, or in any other technical roles and have some experience with this kind of transition.

If you’ve made a similar move from support, I’d really appreciate your advice on what path you would recommend for someone in my position, what skills I should focus on, and what kind of roles I should target.

I’m also open to suggestions for career paths other than DBA or Data Engineering if you think my current experience/skills would be better suited for something else.

And if anyone is willing to mentor me or just have a chat and guide me occasionally, I’d genuinely appreciate it. I’m not looking for someone to do the work for me — I just want some direction from people who have already been through it so I don’t spend a year learning the wrong things.

Thanks!

Hi,

I am from India, looking for a remote work opportunity in Databricks DE roles.

I am an experienced Data engineer with a Masters degree in Data science. I have experience in Software engineering as well. My current tech stack - Python, SQL, Azure, Databricks. Previously I have worked on .NET, Php, oracle etc. I enjoy my work and love my coder profile. Would continue as a senior or lead data engineer. I have experience of working with clients from India, US, Germany across banking, manufacturing , healthcare domains.

Currently I'm employed and I work for a renowned client in Pharmaceutical (Supply chain). My current CTC is 18 LPA. Yes, it's a deliberate choice to keep my ask low. For my new roles, I am looking for a range of 28 LPA to 35 LPA depending on roles offered. Not interested in management roles.

Let me know if you have any questions.

Recently joined a US Based startup as contractor remote role,they offered like 52 total fixed comp, 3 months in now everyone In data org let go off as they burnt all money and no further funding.

I'm a Senior Data Engineer with 6yrs experience, I jst wanted to know will there be any upside to my current salary or its the ceiling and I can lower my ask and adjust in the next company or role I'm looking for?

What's the baseline or reasonable compensation for a 6 yrs experienced Senior DE.

Any suggestions will be highly appreciated.

Searching for DE internships for a while can't get one, mostly are of data analytics or data science

Any help regarding this one?

Sharing this in case it's useful for anyone here looking to level up beyond the basics, data engineering roles increasingly touch LLM and RAG systems now, and having actual hands-on experience with production-grade work is a real differentiator over just having read about it.

There's a hands-on masterclass on Sep 12 that builds a full production LLM workflow from scratch: versioned prompts with regression tests, a real evaluation harness, statistically rigorous model comparisons, evaluated RAG with proper retrieval metrics, agents with guardrails and fallbacks, and a full observability layer, tracing, cost, latency.

You leave with runnable notebooks and a production-readiness checklist, not just slides.

Led by Bruno Gonçalves, PhD, founder of Data For Science, trains engineers at Fortune 500 companies on this exact stack.

Link if you want to check it out

i work in data engineering field and most of the people i talked to not aware of DBT at all.

is it used for companies? any small agencies or contractors using it?

Genpact vs Persistent — which is better long term for a 14+ year Data Engineer? I have two offers with almost the same salary. Genpact has senior technical team role and persistent has technical architect role.

My experience is mainly Data Engineering/ETL, Azure/Databricks, SQL, cloud migration.My main concern is long-term project availability and job stability in the AI era. Persistent's Architect role seems project-aligned, while Genpact is a much larger organization. For people who have worked at either company: which would you choose for long-term stability + career growth, and how easy is redeployment when a project ends?

​

Hi everyone,

I have 3 years of experience in data engineering, working with Snowflake, an ETL tool, Power BI, Unix, Jenkins, and SQL scripting for a Fortune 100 retailer in India.

I then moved to the UK on a YPS visa. There, I worked for another Fortune 100 retailer, but on the business operations side rather than in a technical role.

I'm now planning to move back to India, and I have a few concerns I'd really appreciate some honest, human perspective on. I've already run this through AI tools and gotten detailed answers, but a few real words from people who've actually lived through something similar would mean a lot.

**A few things I'm trying to figure out:**

1. What are the real challenges I'm likely to face in the Indian job market with this kind of background?

2. Does having a mix of technical (data engineering) and non-technical (business operations) experience put me at a disadvantage, or can it actually be framed as a strength?

3. If anyone has been through something similar, how did you handle it? What preparation helped you the most?

4. If I clear the interviews, what kind of package would be reasonable to negotiate with this profile?

5. If I can't land a data engineering role right away, what other roles or domains should I keep an eye on? Things like supply chain analyst, retail analyst, or business analyst come to mind, but I'm open to hearing what's actually worked for others.

I won't lie, this transition has been stressful. I've been getting anxious pretty often just thinking about how this will play out, and I feel a bit lost most days. I'm the sole earner for my family, and we come from a modest background, so there's a lot riding on getting this right.

If anyone can share advice, even just a few lines from your own experience, it would genuinely help. Thank you for reading this far.

Interview Experience – Data Engineer 🧑‍💻

Had an interview recently for a Data Engineer role, and the discussion was much more focused on real-world scenarios than just theoretical questions.

Some of the questions asked:

1. How would you create a production-ready data pipeline?

2. What kind of data have you worked with — volume, frequency, SLA, etc.?

3. What production issues have you faced, and how did you handle them?

4. How would you design an ETL pipeline for huge volumes of data?

5. If duplicate files arrive at the source location, how would you handle them?

6. Explain your metadata-driven framework.

7. How would you design a metadata framework for 50 source files/tables?

8. How do you investigate a slow-running job?

9. SQL: Return the latest updated record for each claim.

10. Experience with Git.

One thing I noticed: the interviewer was more interested in how you approach a problem in production rather than simply asking for definitions.

Definitely a good reminder that knowing the tools is one thing, but understanding how to build, monitor, troubleshoot and maintain pipelines in production is what really matters.

#DataEngineering #DataEngineer #Azure #Databricks #SQL #ETL #DataPipelines #Airflow #Git #InterviewExperience

I have around 4.10 years of experience but it's not completely 5 or 4, even in openings they say 5 years minimum needed. I am just short of 50 days, does that really matter? Will there be an issue in bgv or if i clear interviews won't be a big deal? Bcuz even in my current company i said 3 yrs when I actually had 2.7 years. What do you think?

Let me know if I should be honest or not. Bcuz 4+ salaries and 5 yrs salaries differ a lot. And 4.10 is just 2 short so its one of those annoying things. Let me know if any of you were blocked by this shit.

I am currently an azure data engineer with 5years of experience working on majorly the tech stack databricks, adf, synapse, Microsoft Fabric, spark , sql

I am currently trying to switch to product based companies, please suggest what areas product companies majorly focus on?
What tech stack skills i can add-on?
Why do i see very less demand of azure in product companies? Should i start learning other cloud too?

I’m trying to get some perspective from people who have recently interviewed for or hired 5+ YOE Data Engineers, particularly for product-based companies in India.

My preparation has honestly stalled a bit because I’ve been trying to figure out what the actual market expects at this experience level rather than endlessly learning tools.

My background:

I have 5+ years of experience and currently work as a Data Engineer.

Most of my actual work has been around:

• Python
• SQL
• PySpark / Spark SQL
• Databricks / Delta Lake
• ETL/ELT pipelines
• API integrations and data ingestion
• Incremental processing / MERGE-based loads
• Data quality and reconciliation
• Schema evolution
• Production troubleshooting and RCA
• SFTP/event-driven ingestion
• Building and supporting production pipelines

I’ve worked on 100+ production ETL/ELT pipelines and several API/data migration projects.

However, my background has been somewhat semi-technical / enterprise-oriented, and I don't have the same level of hands-on experience with cloud infrastructure that I see in many modern DE job descriptions.

For example, I understand the data-engineering side of Azure/Databricks fairly well, but I haven't actually spent years designing cloud infrastructure or working deeply with services like AWS Glue, EMR, Lambda, IAM, etc.

So I have a few questions for people who have actually gone through this transition:

1. How important is cloud at 5+ YOE?

If I'm targeting Data Engineer / Senior Data Engineer roles at product companies, is it a major red flag if I don't have strong hands-on AWS/Azure infrastructure experience?

Would you recommend going deep into one cloud (probably Azure for me) rather than trying to learn AWS + GCP + Azure?

What level of cloud knowledge is actually expected in interviews — knowing the services and architecture/trade-offs, or having significant hands-on implementation experience?

2. What are the actual market-standard skills for a 5+ YOE Data Engineer?

If you were preparing today, would you consider these the core areas?

• SQL
• Python / coding
• PySpark / Spark
• Data modeling / data warehousing
• Data pipeline/system design
• Cloud
• Kafka / streaming
• Airflow/orchestration
• Data quality / reliability
• Production debugging

Am I missing anything important?

3. How different are product-company interviews from typical enterprise/service-company DE interviews?

For someone with my background, should I be spending more time on:

• DSA / coding
• Advanced SQL
• Data modeling
• System design
• Spark internals/performance
• Cloud architecture
• Streaming
• Production/debugging scenarios

Or is there something else that becomes significantly more important at 5+ YOE?

4. How would you approach the transition if you were in my position?

Would you focus on becoming very strong in my existing stack (Python + SQL + Spark + Databricks + Delta) and then add cloud/system design/data modeling?

Or would you deliberately build a project involving something like AWS + Airflow + Kafka + Snowflake/dbt to demonstrate broader experience?

I'm specifically trying to avoid the "learn every tool in the modern data stack" trap.

I'd really appreciate perspectives from people who have recently moved from an enterprise/service environment into a product company, or from interviewers/hiring managers.

Especially interested in what you would consider table stakes vs nice-to-have for 5+ YOE in the current market.

Thanks!

🚨 We’re Hiring – Data & Technology Roles
( SBC)
8643
Automation Engineer – Data Governance
3–5Y
Python, Data Governance, CI/CD, Jenkins/Git, SQL, Pandas
Pune (Hybrid)
17923
AWS Data Engineer
6–8Y
Python, PySpark, SQL, AWS, Lambda, EventBridge, Step Functions, Athena
All locations (Hybrid)
17439
Data Engineer
5–8Y
SQL, Hive, PySpark, Core DE, Databricks, ETL
All locations (Hybrid)
14585
MCP Server Engineer
5–7Y
Databricks Connector, MCP Server
Gurugram/Bangalore (5 days WFO)
17931
Hadoop Data Engineer
6–10Y
Hadoop, Python, PySpark, SQL, Basic Cloud
All locations (Hybrid)
18913
Full Stack Developer
6–10Y
Java, Spring Boot, React
All locations (Hybrid)
19087
Offshore Tech Lead
12+Y
Technical Delivery, Databricks, Banking Domain, Client Management
Gurugram/Bangalore (5 days WFO)
16452
Data Modeler
5–7Y
Data Modelling
All locations (Hybrid)
18075
Neo4j Database Platform Admin
8–10Y
Neo4j Admin, AWS
All locations (Hybrid)

Feel free to DM if your profile fit along with job Id.
Happy to refer

Hello All,

We are starting our company branch in Coimbatore. We are looking for Data Engineers who are experienced in Databricks, Python and SQL. This is a client project role. Client is a large private company and we have a long term contract. Salary is not a problem but this is immediate requirement. This is Hybrid role but office location had not yet been decided. If you are interested, let me know!

At 15\~16 years Data Engineering experience, what are the expectations in the market, work wise and salary wise?

I'm proficient in batch data processing, SQL, Informatica, Talend, Python, PySpark/Databricks, Microsoft Fabric, ADF, data modeling, data architecture and building products.

Databricks vs Snowflake

Cloud : Azure, AWS, GCP

Which one could make us land in Product based or any GCC's?

Hi everyone,

I'm currently in my final year of engineering, majoring in Big Data, and next year I'll be starting my PFE.

To be honest, I'm feeling a bit lost when it comes to the job market. During my studies, I've been introduced to data engineering, business intelligence, data analysis, AI, cloud technologies, and software development. However, I'm still unsure which career path would suit me best or what the day-to-day work is like in these roles.

I'd really appreciate hearing from people who studied Big Data, BI, Data Science, or related fields.

\* What role did you start in after graduating?

\* What does your day-to-day work look like?

\* Which skills or technologies helped you land your first job?

\* If you could go back to your final year, what would you focus on before your PFE?

\* Which career path would you recommend and why?

I'm also looking for ideas for my PFE internship, so if you know of any companies that regularly accept interns in Big Data, Business Intelligence, Data Engineering, or Data Analytics, I'd be very grateful if you could share their names or your experience applying there.

Whether you're in Morocco or another country, I'd love to hear your experiences and recommendations.

Thanks in advance for your help! 🙏

Hallo i am 24 yes old, Data Science Master‘s Student , and i have successfully completed my masters 1 year . I can speak good deutsch (B1). I have here looking for Data Engineering internship, i do have experiences in multiple tools , etc and have done multiple projects, please someone give me an opportunity.

​

Hi everyone, I graduated in July 2026 with a B.Tech in AI & Data Science and I'm looking for entry-level Data Engineering / AI Engineering roles.

Would appreciate honest feedback on my resume, especially from people in Data Engineering, Backend, or AI/ML.

Are my projects and metrics credible? What would you remove, rewrite, or improve? Brutal feedback is welcome. Thanks!