r/dataengineersindia

10 YOE - Offer Evaluation (India - Data Engineering)

Got multiple offers and trying to decide based on long-term growth, WLB, and role impact.

1. Optum – 40 LPA + 20% variable + 1L JB

   Role: Senior Data Engineering Lead

2. Novartis – 38 LPA + 10% variable (HR round pending)

   Role: Data Engineering Manager

3. Thermo Fisher – 42 LPA (10% variable included)

   Role: Solutions Architect

Key priorities:

- Career growth (next 3–5 years)

- Work-life balance

- Stability vs learning

- Tech exposure

Which one would you pick and why?

Also, how do these companies compare in terms of culture and growth in India?

Hey everyone,

I’m a BE grad in Electronics & Computer Engineering from a tier 3 college, currently working towards data engineering roles (primarily Pune/Bangalore).

So far I’ve focused on getting comfortable with SQL and Python. Now I’m trying to be more intentional about what to pick up next instead of just following random “DE roadmap” lists.

From what I see, most entry-level roles mention a mix of:

ETL concepts / pipelines

Some exposure to Spark or similar

Basic cloud + data warehousing

What I’m trying to understand is how deep freshers are actually expected to go here vs what’s usually learned on the job.

A couple of things I’d appreciate insight on:

For entry-level roles, what’s the minimum practical stack you’ve seen people get hired with?

Are there certain types of companies that are more realistic starting points for freshers in data engineering?

Appreciate any insights.

Hey everyone,

I recently went through the Amazon Data Engineer interview process (Hyderabad) and wanted to share my experience. I didn’t find many detailed India-specific posts while preparing, so hopefully this helps someone.

---

Round 1 – Coding

The first round was a coding round with basic problems. Think LeetCode easy to medium level. Mostly arrays and hashmap-based questions (similar to 2-sum type problems). It wasn’t heavy DSA—no trees or graphs in my case. The intent seemed to be to check whether you can code comfortably.

---

Rounds 2 & 3 – Architecture + Past Experience

These rounds focused heavily on my previous work. There was a deep dive into the pipelines and systems I had built. Questions were around:

End-to-end design of data pipelines

Challenges faced and how I handled them

Trade-offs in design decisions

A lot of follow-ups came from whatever I mentioned, so being clear about your own work is important. My Mastercard experience carried most of these discussions.

---

Round 4 – Deep Data Engineering Concepts

This was the most intense technical round. The focus was on core data engineering concepts like:

Query optimization

Partitioning strategies

Index vs query rewrite vs data model changes

They were not just looking for answers but for reasoning. For almost every answer, there were follow-up questions asking why I chose that approach over others.

---

Round 5 – Data Modeling

This round was entirely focused on data modeling. I was asked to design a vendor payment system from scratch.

We discussed:

Fact vs dimension tables

Business events and how they map to tables

Granularity decisions

Payment batch vs order-level tracking

Schema design (star vs snowflake)

How to store payment-related information (normalization and security considerations)

SCD types were also asked, so that’s something you should definitely prepare.

---

Round 6 – Bar Raiser (Behavioral)

This was a pure behavioral round based on Amazon Leadership Principles. The questions were detailed and required structured answers with clear examples. This round felt quite important in terms of final decision-making.

---

Overall Takeaways

The process is not focused on heavy DSA. Coding is required, but at a basic level. The main focus areas are:

SQL and data handling

Data modeling

System design for data problems

Real-world experience and decision-making

---

Preparation Advice

Be very strong in SQL (joins, aggregations, window functions)

Know your projects inside out—they will go deep

Be prepared to explain trade-offs, not just solutions

Practice behavioral answers aligned with Amazon leadership principles

---

Overall, the process felt practical and aligned with real data engineering work rather than just theoretical questions.

Happy to answer questions if anyone is preparing.

Hi All,

if you would have been following my posts ,yes I joined tiger analytics today and Accenture HR ghosted me post I went with tiger offer of 29.5 fixed and didn't reply to email,sms and calls .hcl also I had but I wanted to try this organisation once and also 60 days notice period will be beneficial. I have completed my executive MBA too ,so I will start from now only to look and once I get mba degree will look for LSEG,jp morgan, Morgan Stanley, AstraZeneca like organisation.if anyone from these organisations please ping or comment need your guidance.

Everything left to god to decide my fate

I am a data engineer with zero experince in a new data engineering team at our org.

Stack: ADF + Databricks + ADLS Gen2(medallion), serving into Microsoft Fabric.

Our work primarily focus on migrating badly built legacy ETL systems to cloud, also on boarding new sources (emailed xlsx/csv files, SQL servers , SAP, third-party ads and sales APIs) into our data space.

The environment I'm working in:

• No proper requirement gathering- most of the things a with half info requireing a lot of back and forth communication.
• Everthing has to be built form scarcth - so, there are no standards or best practices set
• No project planning - each project is a single jira ticket
• Architecture is just like "here you go - databricks and ADF - use it"
• There is one senior DE but the above but manager doesnot want him to be smart and impactfull - because they wanna secure thier manager layer

I want to grow and learn as a data engineer - both technically and also handling the process side

Would love advice on:

• How do you deal with unclear requirements and no direct stakeholder access — what do you do before writing a single line of code?
• What standards or practices are worth pushing for early in a new DE team?
• Best practices for this stack and multi-source ingestion (APIs, SAP, SQL, flat files)
• How do you make good architecture decisions when there's no proper design stage?
• Resources that taught you to think like a proper data engineer, not just use the tools

Happy to hear from anyone who's been in a "building the plane while flying it" situation. What helped you most?

I’m a Data Engineer with 5 YOE in WITCH.

I was released from my old project on Dec 31 due to vendor change. After that, I was trying to switch while also looking for an internal project.

Got mapped to a new project in mid-Feb, and it was supposed to start in March. But I got an offer from an SBC by Feb end, so I resigned, and was immediately released from that project too.

Now I’m serving my 90 days notice period. With

30 days already done, 60 more to go.

Current situation:

No real work, just login, logout. No one has contacted me for early release and I haven’t asked either.

Meanwhile, I’m also expecting another offer from a different SBC with a slightly better package

My question:

Should I ask for an early release once I get the second offer? Or just stay quiet and complete the notice period? Since I’m not doing any work, can that cause any issues later?

Open for any advice!!!

TL;DR:

5 YOE DE at HCL. On the bench for 90 days notice after resigning. No work, no early release communication, and waiting for a possibly better offer. Should I ask for an early release or just ride it out?

Hi everyone,

I’m currently planning to relocate from the USA to India and am actively looking for Full Stack Developer opportunities. I would really appreciate any referrals or leads.

About me:

• 6 years of experience as a Java Full Stack Developer

• Master’s in Computer Science from USA (GPA 3.8)

• Strong experience in building scalable enterprise applications using Java, Spring Boot, Microservices, Angular, and Python

• Hands-on experience with AWS (EC2, S3, RDS, IAM), Docker, Kubernetes, Kafka, MongoDB, MySQL

• Worked in Agile environments with CI/CD tools like Jenkins and Git

Recent Experience:

• Java Full Stack Developer – USPTO (USA)

• Java Full Stack Developer – Citi Bank (USA)

Tech Stack:

Java, Spring Boot, Microservices, Angular, Python, REST APIs, AWS, Docker, Kubernetes, Kafka, SQL/NoSQL

I’m open to roles in product companies, startups, or enterprise teams across India (Bangalore, Hyderabad, Pune, Chennai, etc.).

If your company is hiring or you can refer me, please comment or DM.

Thanks in advance for your support 🙏

If anyone has experience interviewing or working at Visa as a Data Engineer, could you please share:

What the hiring process looks like (rounds, timeline, etc.)

What kind of technical questions are asked (SQL, Python, system design, etc.)

Difficulty level of interviews

Any tips to prepare

Hi all,

I am currently holding Azure DE ( 4 years exp) offers from Cognizant, Deloitte, and TMUS (T-Mobile US Product). The HR discussion with Tiger Analytics is still pending, and for CGI, the technical rounds are completed, while the managerial round and client interview are still pending.

I have 10 days left in my notice period and would appreciate your suggestions on which would be the best option to choose, especially from a stability perspective.

Hi guys, need your suggestion 🙏

YOE: 7.1 years

Tech stack: Snowflake, dbt, SQL, Python, Data Modeling, Data Warehousing, Azure

I have the following offers:

• EY GDS (Pune, hybrid) – 29 LPA fixed + 10% variable

• Tiger Analytics (Remote) – 32 LPA fixed (project not confirmed)

• Kipi.ai (Remote) – 32 LPA fixed (project not confirmed)

I’m thinking of giving a counteroffer to EY to match the other offers.

Also, which one would be better in terms of tech stack learning and job security? I don’t have a strong preference for remote vs hybrid—my priority is growth and stability.

What would you suggest in this situation? Should I prioritize compensation, learning opportunities, or company stability?

Would really appreciate your inputs 🙌

Hi, I’m looking for paid Data Engineering tasks or support work. I haven’t been able to secure a full-time role yet, but I have completed Data Engineering training and built projects using AWS, SQL, and Python.

I’m willing to assist with real tasks, side projects, data processing, ETL work, or any junior-level responsibilities. I’m open to working for a small stipend, as my immediate goal is to gain hands-on experience while also earning.

If you need help with any work or know someone who does, I’d be happy to contribute.

Hi all,

I just want to understand how to switch a company for that I have prepared a resume but none of them which I applied not getting shortlisted I am having 1.8 yoe with databricks and aws as main skills and data modelling , powerbi .open for any suggestions and which companies to target in the field of Data Engineering?

It would help me a lot

Hey everyone,

I’m currently applying for a Data Science internship at BCG X (Visiting Data Scientist role) and wanted to get a better sense of the interview process.

I’d really appreciate insights from anyone who has gone through it recently (especially in Paris, but all experiences are helpful):

• What are the main stages? (OA, technical interviews, case study, fit, etc.)
• How technical does it get? (ML theory vs coding vs business cases)
• Are there case interviews like in classic BCG consulting roles?
• What kind of problems should I expect (ML modeling, product sense, analytics cases…)?
• Any tips on how to best prepare?

Also, how much emphasis is placed on business understanding vs pure data science skills?

Thanks a lot in advance — any advice or experience would really help 🙏

Hey everyone!

I’ve been in the Data Engineering space for about 15 years now, and for the past 10 years I’ve been helping folks from different parts of the world switch into this field. I’m currently starting a new 3-month cohort and looking for 5 people who are genuinely interested in leveling up their skills.

Happy to share my LinkedIn over DM if you want to check my background. Just a heads up—this isn’t free, but I keep it pretty reasonable (and we can always discuss it).

We’ll have 3 live sessions every week for 3 months, and I’ll also help you out with resumes, mock interviews, and referrals. If you’re curious, we can jump on a quick Google Meet where I can understand your goals and walk you through what we’ll be covering, including the kind of projects and skills we’ll focus on.

Drop me a message if this sounds interesting