Amazon Data Engineer Interview Questions & Practice

Amazon Data Engineer interviews lean heavily on SQL (window functions, aggregations, dedup), data modeling, and pipeline design with AWS services like Glue, Redshift and EMR. Practice the question styles below — each is auto-graded with instant feedback — then check the live Amazon Data Engineering openings.

What to emphasise for Amazon

Amazon works on very large-scale retail, logistics and AWS workloads, so it is worth going in strong on petabyte-scale batch and streaming pipelines, cost-aware design on cloud storage, and clear written reasoning — Amazon publishes its Leadership Principles, so expect behavioural answers to be weighted alongside the technical ones. Treat that as where to spend your last few days of revision, not as a script — the fundamentals below are what actually get tested, and a candidate who can write clean SQL and explain a pipeline end to end does well regardless of the company. Note that interview formats change frequently and vary by team and level, so use this as preparation guidance rather than a guarantee of what you will be asked.

What a Data Engineer interview usually covers

Most Data Engineering loops test four things, in some combination. First, SQL — almost always the deepest-tested skill, with joins, aggregation, window functions and top-N-per-group questions appearing again and again. Second, data modelling and pipeline design: how you would ingest a source, where you would clean it, how you would handle late or duplicate records, and how the tables would be laid out for consumption. Third, coding in Python or PySpark, usually a transformation rather than a puzzle. Fourth, a discussion of something you have actually built, where the interviewer probes your decisions and what you would change.

How to practise so it sticks

Reading solutions creates a false sense of readiness. Write the query yourself, run it, and check the output against the expected result — that feedback loop is what turns recognition into recall. Work in patterns rather than one-off puzzles: once you can write a top-N-per-group query from memory, a whole family of questions becomes routine. Then practise explaining your approach out loud before you type, because most interviews assess your reasoning as much as your final answer. Finish by rehearsing two or three projects in enough detail to discuss the trade-offs you made.

A four-week preparation plan

Week one: rebuild SQL fundamentals — joins, grouping, subqueries and CTEs — until they are automatic. Week two: window functions and the analytics patterns built on them (running totals, ranking, period-over-period, deduplication). Week three: PySpark — the DataFrame API, joins and shuffles, partitioning, and why a job is slow. Week four: system-style design questions and behavioural preparation, plus timed mixed practice so you are used to switching between question types. Throughout, keep a short list of the mistakes you actually make and re-test yourself on those, since that is where the marginal gains are.

Practice challenges

• Find Duplicate Emails (sql, easy, +50 XP)
• Second Highest Salary (sql, easy, +50 XP)
• Latest Order Per Customer (sql, easy, +50 XP)
• 30-Day Rolling Sales Amount (sql, easy, +100 XP)
• 7-Day Rolling Average Sales (sql, easy, +100 XP)
• Customers With at Least One Order (sql, easy, +50 XP)
• Count Orders Per Customer Including Zero (sql, easy, +50 XP)
• Total Amount Spent Per Customer (With Zero) (sql, easy, +50 XP)
• E-commerce: Customers Who Placed an Order (IN Subquery) (sql, easy, +50 XP)
• E-commerce: Count Orders Per Customer (sql, easy, +50 XP)
• ROW_NUMBER: Assign Unique Rank to Each Employee (sql, easy, +50 XP)
• ROW_NUMBER: Find the Highest-Paid Employee in Each Department (sql, easy, +50 XP)
• RANK: Rank Employees by Salary (With Gaps) (sql, easy, +50 XP)
• RANK vs DENSE_RANK: See the Difference Side by Side (sql, easy, +50 XP)
• SUM OVER: Calculate Running Total of Daily Sales (sql, easy, +50 XP)
• SUM OVER: Show Grand Total on Every Row (sql, easy, +50 XP)
• SUM OVER PARTITION BY: Running Total Per Department (sql, easy, +50 XP)
• LAG: Show Previous Day's Sales Amount (sql, easy, +50 XP)
• LAG: Calculate Day-Over-Day Sales Change (sql, easy, +50 XP)
• LEAD: Show Next Day's Sales Amount (sql, easy, +50 XP)

Live Amazon Data Engineering jobs

• Data Engineer, SPTC — Seattle, Washington, USA
• Environmental Data Engineer, AWS Environmental — Austin, Texas, USA
• Environmental Data Engineer, AWS Environmental — Herndon, Virginia, USA
• Data Engineer II - AMZ27757.1 — New York, New York, USA
• Data Engineer II - AMZ27957.1 — Chicago, Illinois, USA
• Senior Data Engineer, DaS FinTech — Bengaluru, Karnataka, IND

What is asked in an Amazon Data Engineer interview?

Typically 2-3 SQL rounds (window functions, joins, aggregation edge cases), a data-modeling discussion, pipeline design with AWS services (Glue, EMR, Redshift, Kinesis), and Leadership Principles behavioral questions.

How much SQL do I need for Amazon DE roles?

A lot — expect to write ROW_NUMBER/RANK dedup queries, rolling aggregations, and multi-join queries by hand. The SQL challenges on this page mirror that difficulty.

Does Amazon ask PySpark in Data Engineer interviews?

Often yes for L5+, especially DataFrame transformations, joins vs broadcast joins, and partitioning trade-offs. Practice the PySpark set below in a real Spark session.

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