r/dataengineersindia

so i’ve been working as an azure data engineer for the past 4 years at service based company. i joined at 4 lpa and got promoted this year, bringing my current package to 6 lpa.

my current project is ending soon and i don’t really have visibility what comes next. there are also ongoing layoffs, so i feel like this is probably the right time for me to switch.

i was actively applying through naukri around march/april and was getting recruiter calls back then, but most companies were asking for immediate joiners. i was getting rejected due to my 3month notice period.

and recently, the calls have almost completely stopped.

3 month notice period is problem and i genuinely don’t feel safe resigning without another offer in hand because my family is financially dependent on me. if things go wrong, i could end up unemployed for months.

for people who successfully switched jobs while having a long notice period:

- did you resign first or wait for an offer?? or is there any other way??

- is there any practical way to increase interview calls? (i'm already updating naukri profile daily)

would really appreciate advice from you guys.

my friend 3+ yoe passed test assessment+technical round+technomanager round apex round+ HC round and now got link for documentation upload....but no salary discussion yet so will they do doc verification first and then discuss package or they are collecting candidate and choosing best among them? it is for senior datascientist role, how much hike he can expect as his base was 13.5 in the previous org and what is their joining bonus

It seems all the companies worth applying for use this awful myworkdayjobs site. All their forms take forever to fill and the questions are filled with irrelevant questions like address, preferred pronouns, willing to work in India with visa.
Plus it takes atleast 2 months for them to basically say you're rejected in a fancier way.

Hi everyone,

I currently have around 3.10 years of experience working in data engineering at TCS. My tech stack includes Azure Data Factory, Databricks, PySpark, SQL, and Azure-based ETL pipelines. I’m also DP-203 certified.

Current CTC: 7.8 LPA
Current in-hand: ~51k/month

I recently received an offer from Cognizant for 11.4 LPA for a Databricks/Azure data engineering role.

I wanted to understand from the community:

- Is 11.4 LPA a fair package for my experience and skillset in the current market?
- Is there still room for negotiation after onboarding formalities have started?
- I don’t currently hold another written offer, though I’m interviewing with other companies.
- Would pushing harder for 12–13 LPA be realistic or risky at this stage?

Would appreciate honest opinions from people working in data engineering / big service companies.

Thanks!

Hi all.

I’m a Data engineer but was put into a Support project 3 months ago.

I am not interested in this role and already feeling pressured. expected to be available 24/7. 5 day WIO. No leave flexibility.

I want to switch but due to the workload unable to focus on study and unable to schedule interviews even on weekends.

How risky is it to resign without an offer in this current market?

Need perspective and suggestions, I have 8 months of savings, will be getting gratuity after resigning.

Hey guys, first time poster here... I need help in deciding between 2 offers I received

So I got an offer from ZS Associates as BTSA - Data Engineer and also another offer from Accenture as Data Platform Engineer

1. ZS Associates

--> Location - Pune

--> 10 LPA fixed

--> 13.55 LPA total compensation (including 150000 yearly retention + 100000 Performance bonus)

--> Role involves:

Big data engineering

Snowflake/Spark/Hadoop

Cloud technologies

Consulting/client-facing exposure

Analytics/data engineering

2. Accenture (AI & Data Practice)

--> Management Level - 10

--> Job Title - Data Eng, Mgmt & Governance Sr Analyst

--> Job Family Group - AI & Data

--> Location - Bengaluru

--> 10.5 LPA fixed

--> 12.7 LPA total compensation

--> Role involves:

Databricks

GenAI

Agentic AI

Enterprise AI/data engineering

AI solution delivery

My confusion:

I genuinely want: strong exposure, challenging work, cloud + AI growth, fast learning.

But at the same time, I do not want completely terrible work-life balance or daily burnout.

I’ve seen a LOT of Reddit posts saying ZS WLB can get very bad depending on team/project, which is making me nervous. At the same time, people also say the growth and learning there is excellent.

Accenture seems safer and more balanced, but ZS feels like it may provide stronger acceleration and consulting exposure.

Also, since I'm based out of Bangalore, the Accenture offer is also an added plus. But I've also heard that the cost of living in Pune is less (plus I'd like to explore Pune as well) than compared to Bangalore. I would want to save up some money since I have upcoming dependencies...

For people who have worked in either/both:

Which role would set me up better long term for cloud/AI/data engineer careers? Is ZS WLB really as bad as Reddit says? How valuable is Accenture’s GenAI/Databricks exposure compared to ZS analytics consulting exposure? Which would you choose if you were early-career (3YOE)? Would really appreciate honest opinions from people with actual experience.

Me - 3YOE previously worked in Fintech as Tech Services Engineer, currently in Bangalore. Tech stack - Databricks, Snowflake, SQL, Python

Also I'm in talks with ZS for a counter offer as well. Accenture didn't up their offer since they said the base pay is higher than ZS

We're a maritime AI company based in Chennai building document-intelligence and decision products for shipping lines (customers across MEA, India and APAC). Adding [1–2] data engineers to the team.

What you'd own:

• Building and running data pipelines on Databricks — PySpark, Delta Lake, job orchestration
• Designing and maintaining the lakehouse / Unity Catalog layer
• Pipeline performance and cost work — cluster sizing, Photon-vs-classic calls, DBU governance
• Ingesting and modelling messy real-world operational data into clean, queryable datasets

You're likely a fit if:

• Hands-on Databricks across multiple production projects — PySpark, Delta Lake, pipelines you owned end to end
• 3+ years in data engineering
• Comfortable being the person the client relies on for Databricks decisions
• Bonus: Databricks certification, streaming/Auto Loader experience, logistics domain exposure

Details:

• Location: Chennai
• Comp: ₹15–30 LPA depending on experience
• Type: Full-time with Exaqube
• You'd join a small, senior data engineering team

To apply: Comment or DM with a short note on a Databricks pipeline you've built and what was genuinely hard about it. If you'd rather not share publicly, here's a quick form instead: Data Engineer - Chennai – Fill out form

company url: https://www.exaqube.com

Happy to answer questions in the thread.

I’ve been working as a Senior Data Engineer and realized most communities are either too generic or not very active.

So I’m building a focused Discord Server for data engineers where we can:

- Discuss real-world architectures
- Do system design / interview prep
- Collaborate on projects (ETL, streaming, etc.)
- Crowdfund on Advanced Courses (Save Money)
- Peer - Peer Mock Interviews to crack Senior DE roles
- Data Modelling Mentorship

If that sounds useful, feel free to join:
https://discord.gg/KbqTJNPRYv

Hi everyone, I'm currently working as Salesforce support at Tech Mahindra and looking to transition into data engineering. I already have my AWS certification, but I'm wondering if it's more advisable to prepare for Azure roles and focus on Databricks jobs or try to move towards AWS Data Engineering roles because I have a certificate in AWS

Has anyone here made a similar switch? Which path would open better opportunities in the current market? Any guidance or resources would be really helpful.

Thanks in advance!

Hi everyone,

I may have an upcoming interview for a Senior Data Platform Engineer role, and I’m trying to prepare properly.

My background is mainly as an Azure Platform Engineer — I’ve worked with Azure networking, CI/CD, security, infrastructure, App Services, AKS, monitoring, etc. However, I was told that a big portion of the interview may focus on Databricks and data engineering concepts.

For those who work with Azure Databricks or interview candidates for similar roles:

• What topics should I focus on the most?
• What kind of Databricks questions are commonly asked in interviews?
• How deep should I go into Spark internals, Delta Lake, medallion architecture, optimization, clusters, Unity Catalog, etc.?
• Are there any must-know concepts or hands-on exercises you’d recommend?

I’d also appreciate any advice on how platform engineering experience can be positioned well for a data platform role.

Thanks in advance!

Hey all,

I recently got an offer from Tata Consultancy Services for a Data Engineering / Reliability Engineering role aligned with a streaming/data platform project.

Current background:

• ~3 years experience as Data Engineer at Quantiphi
• Working on Databricks, Snowflake, ADF, PySpark, FastAPI, BigQuery, distributed data pipelines, optimization work, etc.
• Previous Android dev experience as well

TCS offered me 17 LPA for a Data Engineering / Reliability Engineering role involving Databricks/Spark/distributed systems.

Initially I mostly attended the interviews for market check and backup, because people generally don’t associate TCS with higher packages due to its mass hiring image. But now I’m a bit confused because:

• Compensation is actually decent compared to many service companies
• Role sounds technically better than generic support projects
• It could add large-scale distributed systems and streaming exposure to my profile

At the same time, I’m wondering:

• Can I leverage this offer to get better product/company offers during notice period?
• Does having a TCS offer at this compensation help in negotiating with other companies?
• Is it risky to reject later if I get something better?
• Would you personally choose this over mid-sized startups offering similar pay but less stability?

This is my first major switch, so looking for genuine suggestions from people who’ve switched in the Data Engineering space recently.

Hi I received an invite from Salesforce for a HackerRank General Software Engineer Assessment (IND only)
it looks like have 2 sections having 1 question each
Also the duration is of 75 mins
Can anyone share any input what to expect here ?

Hi everyone,

Hope you are all doing well. I’m trying to move into DE domain. i have 8 years of QA experience. Is this transition really worth it?

Im in 15LPA salary bracket and in QA i get less calls and more than 20LPA I’m not able to cross. Also, quality of work in QA is mostly manual. And I get very less automation opportunities.

So, please let me know how do i plan for this transition is it worth it?

Hi Guys I am currently working for LTIMindtree and deployed to HSBC client.
And have got opportunity/offer from Capgemini for same HSBC project.

Now the question is in this community any of you switched vendors and joinned same client project?

As Coforge conveyed that there is a client side cooling period of 6 months before on boarding again to HSBC.

I only have offer from TCS (which I dont wanna join) and second is capgemini which i think i wont get because of this client conflict
Pls suggest if any one faced this issue

REFERAL: Im a GCP data engineer with 5.1 yoe exp
If there are any open positions pls dm i would appreciate a referral

I have no experience in data engineering but learned it and got the offer any help how to proceed further and have a total of 5 yrs of experience. I welcome all kinds of suggestions.

Holding 2 offers:

1. Product-based company in HVAC – ₹21 LPA fixed, hybrid, flexible timings, 10k employee strength, Senior Data Engineer role.
2. Service-based company – ₹23 LPA fixed, hybrid, 2–11 PM (flexible), 800 employee strength, Senior Data Engineer role.

I initially rejected the service-based company, but they persuaded me by increasing the offer to ₹23 LPA fixed. They are calling me again and again.

My LWD is this Friday, so I’m wondering whether calling the product company and asking them to match the offer would jeopardize things. I’m leaning toward the product company, but the higher number from the service company is making me reconsider. It’s apparently for a US-based client, and they say there’s no need to stay in the office until 11 PM, you can leave by 5:30 PM and punch out through the portal at 11. I only have about 3 days to decide.

YOE: 5.5 years
Current CCTC: ₹9.5 LPA

Hi guys, I got an offer from Amgen for the position of Data engineer at hyderabad office, wanted to know more about the culture, payscale and certification policies here. YOE - 5.5 years

Guys , if anybody from genesys Chennai please comment.. actually I have applied for associate data engineer role and got two mails ..hr like to have zoom call asked for availability, i responded my availability but he didn't replied/got mail from him ..i got laid off sep 25' it is been so hard to get call back...I have around 2 yoe and this opportunity seemed like a putting an end to this gap ...if anybody from gensys Chennai please lmk

TIA!!

Hi everyone,

I’m currently preparing for Data Engineering interviews(currently DE at product based company with 3 yoe), especially rounds focused on system design, scalability, distributed systems, and large-scale data processing.

I’m comfortable with:

Python, SQL, PySpark

Batch + streaming pipelines

Medallion architecture (Bronze/Silver/Gold)

Lambda/Kappa architecture

CDC, incremental loads vs full loads

Partitioning, bucketing, optimization concepts

Data warehousing vs OLTP systems

Airflow orchestration and cloud ecosystem understanding

However, one challenge I’m facing is that in my actual work experience, I haven’t directly handled truly massive-scale systems like:

1. 500TB / petabyte-scale pipelines

2. billions of events per day

3. Extremely high-throughput streaming systems

In interviews, I’m seeing a lot of questions like:

“How would you scale your pipeline from 500GB to multiple TBs?”

“How would you handle billions of rows in S3 without full scans?”

“How would you detect duplicates/missing data efficiently?”

“How would you design real-time fraud detection pipelines?”

“How would you optimize shuffle-heavy Spark jobs?”

“How would you handle skew, late-arriving data, retries, SLA failures, and observability?”

My questions are:

What is the best way to genuinely build system design thinking for large-scale data engineering systems if you haven’t worked at that scale directly?

How do experienced engineers think through:

scalability

distributed bottlenecks

non-functional requirements

reliability

throughput vs latency tradeoffs

operational concerns during interviews?

Is there a practical way to get hands-on exposure to these concepts outside production environments? For example:

open-source projects

simulation environments

cloud labs

datasets

personal projects

system design exercises

Kafka/Spark mini architectures etc.

How would you recommend preparing specifically for:

incremental loads vs full loads

CDC pipelines

data quality validation at scale

Spark optimization

partitioning strategy

real-time + analytical hybrid systems

handling missing/corrupted/late data

observability and monitoring

near real-time data pipelines with high traffic

Would really appreciate advice from people who’ve worked on large-scale systems or cracked strong DE/system design interviews recently.

Thanks in advance!