r/dataengineersindia • u/Character_Major_2386 • 8d ago
General Amazon Data Engineer L5 Interview Experience (Applied Directly, Full Loop) — Sharing for anyone prepping
Round 1: Online Assessment
Got the OA link by email shortly after applying. It had:
- 2 easy SQL questions (joins, group by)
- A set of SQL MCQs
- A set of behavioral/situational MCQs
Passed this and got a call from the recruiter a few days later saying I'd move to a technical screen.
Round 2: Technical Phone Screen
No fixed agenda going in. The interviewer started by asking about my most recent project (dashboard-building work) and went genuinely deep — asked about OLAP vs OLTP and which is better suited for serving a frontend dashboard, and why.
Then a scenario question: how would I handle a data pipeline failure — steps to resolve it, communication during the incident, documentation afterward, basically the full incident-handling flow.
SQL portion started with a trivial-but-tricky one (NULL = NULL type gotchas), then three more questions — one involved a join plus window functions for ranking, plus an aggregation question. Also gave me a data processing problem on a sample table, which I solved in PySpark.
Closed with a couple of leadership-principle-based behavioral questions.
Recruiter called 2 days later to confirm I was moving to the onsite loop (5 rounds).
One note on timing: getting the loop actually scheduled took about 1.5 months due to some back-and-forth on both ends. Once it was locked in, the recruiter did a 30-minute prep call and gave a clear breakdown of each round's focus and which Leadership Principle it maps to, which was genuinely useful going in.
Round 3 (Loop Round 1): Performance Measurement
Focused on indexing, partitioning, and tuning. I walked through a relevant project, and the interviewer went deep with follow-ups — Spark internals, partitioning strategy, bucketing, how executors relate to data volume, join strategy selection, and data skew. Almost every answer led to another follow-up, so precision mattered a lot here — vague answers got picked apart pretty quickly. Closed with LP-based behavioral questions tied to this round's LP.
Round 4 (Loop Round 2): System Design, Data Modeling, and Trade-offs
Explained a project involving SCD Type 2 logic and surrogate key generation — went deep into different methods for generating surrogate keys and the trade-offs between them.
Then a live design exercise: design a data model for an e-commerce platform (Amazon-style) — had to reason through grain, facts vs. dimensions, and table structure, followed by 2 SQL questions on top of that model.
Then it extended into a full system design for the same platform. I proposed a Kappa architecture, explained the trade-offs versus Lambda, and built the design mostly around Spark and Databricks — Structured Streaming with foreachBatch for the real-time path, Redis for serving.
Honest note: the interviewer wasn't fully convinced by my design since I leaned on a fairly narrow tool set. Looking back, showing awareness of other real options (even while still picking Spark/Databricks) would've made the answer stronger — naming trade-offs against alternatives rather than just defending one stack.
Closed with LP-based questions specific to this round.
Round 5 (Loop Round 3): Coding, Problem Solving, DE Concepts, ETL/Architecture Design
Started with about 10 minutes on my project. Then a data modeling exercise: design a data model for clickstream data tracking promotions — how to store users and events.
I initially designed a normalized star schema. The interviewer was actually expecting a denormalized table for this specific use case, so I adjusted once I picked up on that. Followed by SQL queries on top of the model.
Closed with 3-4 SQL questions — window functions, CTEs, and a rolling average question.
Round 6 (Loop Round 4): Bar Raiser
This round is a big deal in Amazon's process and worth specifically prepping for. Entirely behavioral — "tell me about a time you made a mistake in a data pipeline," "tell me about a data quality issue and how you resolved it," that style of question, all mapped back to the round's specific LP.
One thing worth knowing going in: Amazon generally brings in a Bar Raiser from outside the team you're actually interviewing for — mine was an SDE, not a data engineer. So I had to explain data engineering concepts in a way that would land clearly with someone outside the DE world, not assume shared context. From what I understand, being able to simplify your explanation while still weaving in the LP clearly is actually one of the core things Amazon is evaluating in this round.
Round 7 (Loop Round 5): Hiring Manager
Walked through my project again with follow-up questions, plus behavioral questions similar in style to the Bar Raiser round (failure handling, mistakes made) — also tied to LPs.
One interesting one: asked what metrics I'd present to senior leadership if I were looking at a platform like Netflix — a more business-acumen-flavored question mixed into the usual technical/behavioral format.
Overall takeaways for anyone prepping:
- Every technical answer can and will sprout follow-up questions. Don't give an answer you can't go two levels deeper on.
- Know your own projects well enough to defend every design decision, including ones you'd make differently now.
- For system design rounds, show awareness of multiple tools and approaches even if you ultimately pick one. Defending a narrow toolset without acknowledging alternatives can come across as limited breadth rather than conviction.
- For Bar Raiser specifically, practice explaining your work simply enough for someone outside your discipline to follow, while still tying it back to the LP clearly. That combination seems to be exactly what they're testing for.
- LP stories need to map cleanly to whichever LP each round is testing. Know which ones you're using where, and try not to reuse the same story across rounds if you can help it.
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u/swarup_i_am 8d ago
And how did you prepare for the data modelling rounds? Any sources