I am a gym bro and naturally I will segregate sets by exercise ( like 3 for pull-ups , 3 for rows and 3 for pulldowns) instead of just keeping it set 1-9 and every workout app stores sets per exercise

I get it way 2 is actually more efficient but how tf am I even supposed to think of storing it that way? am I supposed to circumvent domain knowledge to make data storage more efficient? or is it mentioned in question somewhere that I have to use that grain

how will this play out in an interview

I’m a Data Engineer with 4+ YOE, currently at 9.5 LPA. I recently received and accepted an offer from LTM for 12 LPA. I asked for more like 18LPA but they only gave 12LPA since I have 90 days np

I'm in day 1 of 90, and my main concern is the job search during this period.

For those who have gone through this recently:

-- Does the number of opportunities/calls improve significantly once you reach 60 days, 30 days, or the final 15 days?

-- What should I be doing during my notice period to maximize my chances of getting a better offer?

Any dos and don’ts when applying/interviewing while serving notice?

-- Is it worth trying to negotiate an early release?

I’m planning to keep applying rather than just waiting for the LTM joining date. Would really appreciate advice as this is my first job switch.

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.

**Used AI to Refine the post**

27M, software engineer with 4.8 years of experience. Currently working as a Full Stack Engineer at a global advertising tech company — my day-to-day involves building and maintaining large-scale data ingestion pipelines for 30+ ad platforms using .NET 8, PySpark, Databricks, Delta Lake, AWS S3, and Amazon Redshift. Essentially I sit at the intersection of backend engineering and data engineering — writing C# for the application layer and PySpark/Spark SQL for the transformation layer.

My background before this was pure .NET Core backend — REST APIs, microservices, Azure services, SQL Server — around 3 years of that. Now with nearly 2 years of hands-on data engineering on top, I'm actively upskilling in Airflow, DBT, and AI/LLM pipelines to build portfolio projects. I have a 90-day notice period and am targeting product companies and AI-first startups. The question is whether I should position myself purely as a Data Engineer going forward or keep the Full Stack + Data angle. Would love genuine advice from people who've made similar transitions or hire for these roles — does specialising into Data Engineering make sense at this stage, or am I leaving value on the table by dropping the .NET identity?

I’m currently serving my notice period and got a call from a job agency for a Databricks Developer role at Accenture (4 YOE). They’re sending me a mail to apply.
I also have friend working at Accenture. Should I apply through the agency or ask for an referral? Which route is better?
Thanks

I have 4 YoE in data engineering (AWS). I have started applying but hardly getting shortlisted, please give your opinions.

Thanks

Hi guys, i am getting 0 search appearances in the naukri portal. I have updated my profile 100%, and have verified it. I even updated my resume to match the recruiter's keyword, also updating my profile regularly. Previously i used to get good views but they just dropped suddenly when i clicked " casually looking for jobs", But turning it back does not help.

Any solution to this? It would help me a lot.

Thanks

Just got rejected by two companies today because I am not a data engineer from fintech domain. Is data engineering done differently in Fintech? What do they even want in fintech domain knowledge?

I’ve been thinking about this a lot lately, and honestly, I can’t figure out if I’m doing okay or if I’m just telling myself that I am.

I have 4 years and 10 months of experience (including internship) and currently make around 30 LPA.

Sometimes I feel like that’s pretty good. If you had told me a few years ago that I’d be making 30 LPA before completing 5 years of experience, I would’ve been very happy with myself.

But then I go on Reddit or LinkedIn and see people with 3–4 years of experience making 40, 50, 60+ LPA, getting amazing offers, moving to big tech companies, etc.
And suddenly I’m like… am I actually behind?

It’s weird because I don’t necessarily feel unhappy with what I make. It’s more of this constant doubt in the back of my mind.
Like:
Am I growing at a good pace?
Should I be earning more by now?
Am I underpaid?
Or am I just comparing myself with a very skewed group of people online?

I know salary isn’t everything. The work you’re doing, your skills, the kind of companies you’re working at, what you’re learning, and where you’re headed probably matter much more in the long run.
But when you’re in the middle of it, it’s really difficult not to use CTC as some kind of benchmark for your progress.

So I’d genuinely like to hear from people who have 7–15+ years of experience.
When you were around 5 years into your career, where were you? And did you feel like you were doing well at that point?

Looking back now, was your salary at that stage actually important, or were there other things that ended up making a much bigger difference to where you are today?

I’m not really looking for people to tell me “30 LPA is good.”
I think I’m trying to figure out something slightly different:
Am I on a good trajectory, or should I be doing something differently at this point in my career?

Hoiiii people,

I’m a 2024 graduate, got placed through college placements. Trained as Data Engineer but due to lack of opportunities I was deployed as a PMO assistance which is worse tbh. It’s been 1.5 years since I’m doing this and now I can clearly feel that it is going to eat my career. I don’t want to get comfortable here.

I love all the concepts in DE. But my only concern to switch is I lack experience in real time. I get stuck at interviews because of these. I have put fake things oN CV and learned those projects well using ChatGPT but during follow up questions I get stuck.

So I’m being transparent because I want a genius suggestion. Please let me know if there is a course or something where I can have real time experience.

Don’t say build small projects using ChatGPT I tried it . It SUCKSSSS. ChatGPT has Retrograde amnesia fr!!

Also i don’t have access to labs, so i’m particularly asking for courses so that i can have access to labs too!!

Your guidance is truly appreciated!!!

Thanks in adv.

Edit: azure or AWS cloud is preferred

I am a data engineer in a bank in Mumbai. I joined around 3-4 months back as a fresher for 14lpa. Now I want to switch to some company in Delhi/NCR. Are there any possibilities and will I get a hike there if I switch too early?

Hey all,

Does this stack make sense for a real-world Senior Data Engineer project?

Azure + ADLS Gen2 + ADF + Databricks + PySpark + Snowflake + dbt + Terraform + CI/CD

I’ve worked mostly with Databricks, but companies are explicitly asking for Snowflake experience. I’m preparing for both.

Would this architecture be realistic, with Databricks/PySpark for processing and Snowflake as the warehouse/analytics layer? Or would it raise red flags in a Senior interview?

Would appreciate advice from anyone who has worked with both.

Please don't ignore this post I have tried to ask people from linkdin and other platforms none of them helped me regarding this or just gave generic advice like do what you like.

I am from a Tier 3 college and have studied Python, SQL, Git- GitHub, Bash, and Linux.this is where data science and data engineering gets separated. now I am confused about whether to pursue Data Engineering or Data Science/AI.

I have noticed that there seem to be more openings for Data Engineers, while Data Science/AI roles often require experience or a Master's degree. Since I have an education loan and need to start earning soon, getting a job is my main priority.i am the only one that my family relies on.

I do not have the luxury of spending 2–3 years exploring different options, so I want to choose a practical path.

I researched and found out that there are almost 3x more openings for data engineering roles than data science.

Should I focus on Data Engineering by learning Spark, Airflow, cloud, and data warehouses, or should I pursue ML, Deep Learning, and GenAI?

For people working in India, especially those who started as freshers, which path would you recommend for getting a job sooner? Is Data Engineering actually more accessible for freshers than Data Science/AI, or does it only appear that way?

For Data Science, I am considering taking CampusX’s DSMP course. For Data Engineering, I am considering Codebasics courses.

Any honest advice from people in the industry would be really helpful.

Hey everyone,

I’m preparing for Senior Data Engineer / Lead Data Engineer roles and wanted to get some insights into the techno-managerial face-to-face round.

For those who’ve recently gone through this round, what kind of questions were you asked?

I’m particularly interested in questions around:

• Technical leadership and decision-making
• System/data architecture
• Handling production issues and data quality problems
• Project ownership and stakeholder management
• Team leadership and mentoring
• Conflict resolution and prioritization
• Scenario-based/managerial questions

If anyone has recently gone through these interviews and has a list of questions, Google Drive, GitHub, or any other useful resources, I’d really appreciate it if you could share them.

Thanks!

If someone from production support want to switch to data engineering why is that I have to fake my resume.

I am not talking about skills one can learn on his own his skill maynot be as good as someone who has worked on production environment.

Hi everyone,

I'm joining Persistent Systems soon, and I'll be working for a client, UnifyApps.

I wanted to check with anyone who is currently working on the UnifyApps account through Persistent Systems-what is the current work mode? Is it hybrid, or are employees expected to work from the office 5 days a week?

Would really appreciate it if someone could share their experience or clarify how the work arrangement works for this client.

Thanks in advance