How Amazon interviews Data Engineers

Amazon publishes its general interview process and separate preparation pages for a few roles. It does not publish a Data Engineer specific page, so the stages below are the general process; screen, coding, system design, behavioral and onsite are preparation categories on this page, not Amazon's round names.

• Phone screen. A 30- to 45-minute discussion, typically with a recruiter or hiring manager, and your first live interaction with Amazon.
• The loop. The final and most comprehensive stage: four to six interviews lasting 45-60 minutes each, where each interviewer assesses different aspects of your skills and experience.
• The Bar Raiser. An objective interviewer from outside the hiring team takes part in the loop to ensure Amazon's hiring standards are maintained.
• Debrief and feedback. The panel holds a debrief after the final interview. Amazon typically provides feedback within two business days after phone interviews and within five business days after the loop.

For comparison, Amazon does publish that Software Development Engineer roles include an online assessment with a coding test before the loop. Whether a Data Engineer candidate receives one is not published, so prepare as if you might.

Two published facts govern every Amazon interview. The 16 Leadership Principles are the foundation of every hiring decision, and the cornerstone of the interviews is the STAR method: situation, task, action, result. In a loop where each interviewer assesses a different aspect, that means each 45-60 minute conversation pairs a technical area with principles, and each story you tell needs a result you can state.

Data Engineer interviews in general assess:

• SQL and data modeling. Correct queries under time pressure and schemas that survive change.
• Pipelines and orchestration. Idempotent reruns, backfills, late data, and what you do when an upstream source lies.
• Distributed data systems. Partitioning, storage formats, batch against streaming, and cost.
• Data quality and ownership. Detection, communication and prevention, which is where the principles about ownership and earning trust find their evidence.

Amazon's published software development topics list databases and distributed computing alongside data structures and algorithms, and says interviewers look for the ability to apply knowledge to solve problems rather than memorized details. That standard is a safe one to prepare against for a data role.

Amazon publishes detail for SDE roles that it does not publish for Data Engineers, which makes the comparison concrete on one side only.

• What Amazon publishes for SDE II: an online assessment of 90 minutes for two technical questions, followed by 20 minutes of systems design scenarios and an 8-minute Work Style Survey; a loop of four 55-minute interviews with members of the software development community; syntactically correct code rather than pseudo code; and at least one question on software systems design.
• What that suggests for a Data Engineer, as general practice rather than Amazon policy: expect the same insistence on working code, expect design questions to be about data flow, storage and freshness rather than request serving, and expect SQL and data modeling to take the place of some of the algorithm breadth.
• What stays the same across every Amazon loop: the principles, STAR, the Bar Raiser and the debrief.

Amazon publishes the pieces but not the total: a 30- to 45-minute phone screen, a loop of four to six interviews of 45-60 minutes, feedback within two business days after phone interviews and within five business days after the loop. Scheduling between those stages is not published.

The difficulty is stamina and consistency. Four to six interviewers each assess a different aspect, one of them is a Bar Raiser from outside the team, and all of them expect stories in STAR form with a real result. A data engineer who can pair every technical answer with a principle-shaped example from real work is prepared for the format; one who has only solved problems in isolation is not.

Build the preparation around the two published constants and the four areas the role is judged on.

• A STAR story bank. For each of the 16 Leadership Principles, prepare at least one story from data work with a measurable result: a pipeline you made reliable, a quality incident you owned, a cost you cut.
• Working code. Practice SQL and transformation problems in an environment that runs them, since Amazon's SDE guidance asks for syntactically correct code, not pseudo code.
• Design with numbers you own. Prepare two or three data systems you can draw, with their failure modes and the trade-offs you made.
• A possible assessment. Amazon does not say whether Data Engineers get an online assessment; prepare a timed coding session anyway.
• Rehearse the loop. A mock interview with SubcueAI helps you practice back-to-back STAR answers and design questions before facing four to six interviewers in a day.