Amazon Data Engineer interviews typically run 3–6 rounds: online SQL assessment, recruiter screen, technical phone screen, and a hiring manager/panel loop. The process spans several weeks and is distinguished by heavy Leadership Principles emphasis throughout every stage.
$103K
Avg. Base Comp
$216K
Avg. Total Comp
3-6
Typical Rounds
3-6 weeks
Process Length
What consistently stands out across Amazon Data Engineer experiences is that the Leadership Principles aren't a soft add-on — they're a structural pillar woven through nearly every round. Multiple candidates reported behavioral probing well outside any dedicated HR screen. One described the final panel as "almost entirely behavioral," and another noted that even technical conversations included persistent LP follow-ups around conflict, ownership, and tradeoffs. The bar raiser round, which several candidates encountered in fuller loops, treats past project clarity and demonstrated ownership as rigorously as any coding question.
On the technical side, SQL is the consistent baseline, but the flavor matters. Window functions, deduplication, and query performance optimization for large-scale data appear far more than basic joins. One candidate specifically noted the assessment leaned situational — not "write this query" but "here's a business problem, now solve it." What's genuinely surprising is how much the scope can shift across loops: at least one candidate hit statistics and probability questions in a DE process, and system design showed up alongside Python scripting and data modeling in the same loop. PySpark came up in candidate discussions even when it wasn't formally tested, suggesting it lives just below the surface.
The thing that actually breaks candidates here is the simultaneous breadth and behavioral depth. Amazon's DE loop can span SQL, Python, data modeling, system design, and LP storytelling — sometimes within the same conversation. Candidates who treat technical and behavioral prep as separate tracks tend to get caught when an interviewer pivots mid-round. The process rewards people who can move fluidly between "here's how I optimized this pipeline" and "here's what I owned and how I'd do it differently now."
Synthetized from 6 candidates reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
My interview process at Amazon for a Data Engineer role was pretty structured and had multiple rounds. It started with a recruiter screen, then moved into a technical phone interview that ran longer than I expected, around 70 minutes. After that, there was a hiring manager or panel-style round, and the process could also include a virtual onsite with several interviews. The overall flow felt professional and consistent, but it was definitely thorough. In the technical round, I was asked five data structure questions, plus one SQL theory question focused on joins, and there was also some discussion of my past project work. The coding portion felt more like a fundamentals check than a single deep algorithm problem, but it still required being quick and accurate under time pressure. In the later rounds, the emphasis shifted toward Amazon Leadership Principles and situational judgment. I was asked behavioral questions like describing a time I disagreed with a decision and how I handled it, and there were also basic engineering questions tied to the role. For more infrastructure-oriented data roles, the interview can get very specific about operational knowledge, including topics like data center operations, HVAC, power distribution, and even how a diesel generator works, so the exact technical depth seems to depend on the team. Overall, I’d call it moderately difficult to challenging. The hardest part was balancing coding, SQL theory, and behavioral prep, since the interview wasn’t just one type of assessment. I did not get an offer. If you’re preparing, make sure you can explain your projects clearly, answer Amazon-style behavioral questions in STAR format, and be ready for both data structure questions and SQL join theory. If your team is infrastructure-heavy, it also helps to review the operational basics specific to that environment.
Prep tip from this candidate
Be ready for a long technical round that mixes multiple data structure questions with SQL join theory and project discussion. Also prepare STAR-format stories for Amazon Leadership Principles, since behavioral and situational questions were a clear part of the process.
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Synthesized from candidate reports. Individual experiences may vary.
An initial call with a recruiter to discuss your background, preferences, and the role itself. This is largely informational and non-technical, and is sometimes preceded by a few written screening questions sent over email.
A HackerRank or Amazon-proprietary platform assessment covering SQL (joins, aggregations, window functions, deduplication), Python scripting basics, and MCQ-style scenario questions on topics like database security and product analytics. This stage is used as an initial filter before live interviews.
A structured live technical interview covering SQL query writing and optimization, Python scripting, and data modeling concepts. May also include basic networking questions and some behavioral or Leadership Principles follow-ups mixed in.
Multiple rounds (typically 2-3) covering SQL performance optimization for large datasets, Python coding (LeetCode-style), data modeling, system design, and occasionally statistics or probability questions. Leadership Principles behavioral questions are woven throughout each round with deep situational follow-ups.
A panel or back-to-back session with the hiring manager and sometimes a Business Intelligence Engineer or bar raiser, focused heavily on Amazon Leadership Principles. Expect in-depth situational questions on past projects, ownership, cross-functional communication, risk-taking, and measurable business outcomes.