Amazon Data Engineer candidates report SQL-heavy screens, Python and data modeling questions, and Leadership Principles behavioral questions across the loop. Reported round counts range from three to about five.
$130K
Avg. Base Comp
$190K
Avg. Total Comp
3-5 rounds
Typical Rounds
Not reported
Process Length
Candidates describing the Amazon Data Engineer interview most often point to SQL under time pressure paired with Leadership Principles questions. Several reports begin with an online SQL assessment. One candidate describes it as situational and multiple-choice on Amazon's own platform, with scenarios on joins and aggregations, database privilege management and product analytics using user behavior data. Another describes a HackerRank-style SQL test followed by a live screen. Later SQL questions reportedly include window functions, left versus right joins, query performance optimization for large data volumes, and a sales-table problem asking for profit per product per year and the highest profit per product each year. Practice beyond basic queries.
Python appears in several accounts, and difficulty varies. Some candidates describe easy scripting questions, including a first-recurring-character problem and a scripting task tied to how an alerting system would work. One report mentions LeetCode-style DSA tested in depth. Data modeling comes up in phone screens. One candidate also had a system design round and an unexpected statistics and probability round. Another account lists database operations and Databricks familiarity as helpful.
Behavioral content is the most consistent theme. Candidates report questions on conflict with a teammate or stakeholder, ownership, a favorite project and what you would do differently, multi-source data integration hurdles, cross-functional alignment, and business outcomes of past work. Prepare several specific STAR stories with metrics, since one candidate describes interviewers probing with multiple follow-ups.
Reported round counts range from three to about five, and the technical mix shifts between processes. Prepare each area rather than betting on a single format.
Synthesized from 44 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
The process kicked off with an online assessment that tested SQL and data engineering fundamentals, which felt more like a screening to verify baseline knowledge than a filter. After that passed, I moved into four interview rounds, each with a distinct focus. The first phone round was straightforward — medium-level SQL questions, mostly around joins and window functions applied to Amazon business scenarios. Nothing too exotic, but they wanted to see how I'd approach optimizing queries and think through real data problems.
The technical rounds got progressively deeper. One session focused on Python, specifically data manipulation and wrangling — think pandas workflows and handling messy datasets. Another round was heavier on system design and data modeling, where they asked me to think through warehouse architecture, ETL pipeline design, and how I'd structure schemas for scale. They weren't looking for a perfect answer; they wanted to see my reasoning and how I'd trade off complexity, cost, and query performance. A Leetcode medium problem also came up — not Amazon-specific, just a standard coding challenge to verify I could write clean code under time pressure.
What surprised me most was how deliberately they tied everything back to Amazon's Leadership Principles. Every question, especially in the later rounds and the bar raiser round, had a behavioral undertone. They'd ask a technical question but were really evaluating how you think about customer obsession, ownership, and bias for action. The bar raiser round in particular felt like a deeper dive into data warehousing and big data concepts — they wanted someone who could own a data problem end-to-end. Manager and HR rounds rounded things out, but by that point it was more about fit and logistics. Overall the process felt streamlined and structured, though intensive. If you make it through all the rounds, they're clearly serious about you.
Prep tip from this candidate
Focus on SQL optimization and window functions applied to real business scenarios, not just abstract LeetCode. Be ready to discuss data modeling and ETL pipeline design at a system level, and prepare to articulate how your technical decisions align with Amazon's Leadership Principles — especially ownership and bias for action — since those principles are woven throughout every round.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amazon
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| Find the First Non-Repeating Character in a String |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial recruiter conversation about their background, preferences and the role. One candidate describes it as non-technical, and another received a few screening questions beforehand. Be ready to summarize your data engineering experience clearly.
Some candidates report an online SQL assessment early in the process. One describes situational, multiple-choice style questions on an Amazon platform covering joins, privilege management and product analytics. Another describes a HackerRank-style SQL test. Format may vary.
Candidates report a live technical screen combining SQL, Python and data modeling. One describes an easy Python question, two easy-to-medium SQL questions, a data modeling question and a Leadership Principles section. Another describes SQL focused on query optimization at scale, a Python scripting question and a conceptual data modeling discussion. Expect time pressure and be ready to explain your reasoning aloud.
One candidate describes about five rounds mixing technical content with Leadership Principles. These included a profit-per-product SQL question, a first-recurring-character Python question, a statistics and probability round and a system design round. Another describes in-depth technical rounds covering SQL, Python, DSA and database operations. Exact structure likely depends on the team.
One candidate reports a final round with the hiring manager and a Business Intelligence Engineer that was almost entirely behavioral. It covered requirements, multi-source data integration hurdles, cross-functional alignment and business outcomes. Another mentions hiring manager and bar-raiser-style rounds. Prepare structured STAR stories with concrete metrics.
