Amazon Data Engineer II interview experience
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.
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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.
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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.
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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.
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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.
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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.
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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
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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
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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.