How Amazon interviews Data Analysts

Amazon publishes its general process and preparation pages for a few roles, but not one for Data Analysts, so the stages below are the general ones. Screen, take-home, case, 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 the first live interaction with Amazon.
• The loop. The final and most comprehensive stage: four to six interviews of 45-60 minutes each, with each interviewer assessing different aspects of your skills and experience.
• The Bar Raiser. An objective interviewer from outside the hiring team joins the loop to ensure hiring standards are maintained.
• Debrief and feedback. The panel debriefs after the final interview; Amazon typically provides feedback within two business days after phone interviews and within five business days after the loop.

Amazon does not publish whether Data Analysts receive an online assessment or a take-home before the loop.

The 16 Leadership Principles are the foundation of every hiring decision, and the STAR method is the cornerstone of the interviews. For an analyst that turns each interview into a pairing: a technical area on one side, a principle-shaped story with a stated result on the other.

Data Analyst interviews in general assess:

• SQL and data cleaning. Joins, aggregations, window functions, and catching the duplicates and nulls before they reach a report.
• Metrics and dashboards. Defining a metric precisely and knowing when it is misleading.
• Communicating findings. Turning a result into a decision someone else can make, which is where principles about customers and earning trust get their evidence.
• Business judgment. Choosing which analysis matters most and being able to defend the choice.

Amazon's published guidance for its Applied Scientist loop says each interviewer typically asks two or three behavioral questions. That is a different role, but it is the clearest published signal of how much of each Amazon interview is behavioral, and an analyst should plan for a similar share.

Amazon's published process is the same across these roles; the emphasis inside the interviews differs, and the following is general practice rather than Amazon policy.

• Compared with Data Scientist and Applied Scientist interviews, expect less modeling and experiment design and more metric definition, descriptive analysis and recommendation. Amazon publishes a 60-minute technical phone screen with a senior leader for Applied Scientists; it publishes nothing equivalent for analysts.
• Compared with Data Engineer interviews, expect little on pipeline architecture and much more on interpreting data that already exists, including its flaws.
• Compared with other companies, the constants are the principles, STAR, the Bar Raiser and a loop of four to six interviews. The volume of behavioral questioning is the thing analysts most often underestimate.

Amazon publishes the pieces rather than the total: a 30- to 45-minute phone screen, a loop of four to six interviews of 45-60 minutes each, and feedback within two business days after phone interviews and within five business days after the loop. The gaps between stages are not published.

The difficulty is consistency across a long day. Several interviewers, one of them from outside the team, each assess a different aspect and each expect STAR-form stories with results. An analyst whose examples all come from one project will run out; prepare stories from several pieces of work, each with a number at the end.

Prepare around the published constants and the four areas of the role.

• A STAR story bank. One story per Leadership Principle from analytical work, each ending in a measurable result: a metric fixed, a decision changed, a cost avoided.
• SQL aloud. Practice queries while narrating the checks you would run on the output.
• Metric definitions. For each metric on your resume, know the definition, its weaknesses and a decision it drove.
• Judgment cases. Practice choosing among several possible analyses and explaining the choice in terms of customer impact.
• Rehearse the loop. A mock interview with SubcueAI lets you practice a run of behavioral and technical questions in one sitting before the real four to six interviews.