Amazon Business Analyst candidates report a mix of assessments, live SQL or Excel work, and Leadership Principles interviews. Prepare clear project stories and explain analytical reasoning aloud.
$108K
Avg. Base Comp
$136K
Avg. Total Comp
4-6 rounds
Typical Rounds
2 weeks
Process Length
Amazon Business Analyst interviews reported by candidates commonly combine practical analytics with detailed behavioral discussion. Several candidates describe an assessment before live interviews, while others report recruiter or hiring-manager conversations first. The technical emphasis varies by team: candidates reported live SQL involving joins, CTEs, subqueries, window functions, query explanation, and in one case optimization. Excel tasks and business cases also appear, from common formulas to profit and percentage calculations.
The most consistent preparation theme is specific, defensible examples from your own work. Candidates repeatedly reported Leadership Principles questions with follow-ups on their individual contribution, decisions made with incomplete information, stakeholder conflict, deadlines, customer impact, and project timelines. Use STAR to give the context, your actions, and measurable result, then be ready to explain why you chose that approach.
Interview sequence and technical depth varied across candidates and teams. Practice translating a business problem into an analytical approach, writing or explaining SQL live, and discussing prior projects without relying on a generic summary. For case-style prompts, show the calculation or reasoning clearly rather than jumping straight to a conclusion.
Synthesized from 34 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
The process was much more analytical and results-focused than I expected for a Business Analyst role with Amazon Music. A recruiter contacted me directly rather than my applying through the site, and they were very involved throughout: they helped me prepare for the questions and checked in after each stage. I began with an HR phone screen, followed by an online email-style task. That exercise felt somewhat unrelated to the actual job, but it involved working through an email scenario with a lot of numbers.
After passing that portion, I went through the loop, speaking with several people I would potentially work with as well as an independent Amazon interviewer. The interviews were heavily behavioral, but the emphasis was on demonstrating measurable impact. I was asked to describe a time I improved a system or process, including the steps I took and the result; a situation where I had to persuade coworkers who disagreed with my idea; and a time I went beyond what was asked of me on a task.
For me, the results-driven framing did not align especially well with the experience I brought to the table. I ultimately declined the offer. My biggest advice is to prepare concrete stories that clearly quantify the outcome of your work, especially around process improvement, influencing others, and taking initiative, and be ready for a numbers-heavy email task before the interview loop.
Prep tip from this candidate
Prepare quantified examples of improving a process, persuading coworkers who initially disagreed, and going beyond assigned work. Also expect a numbers-heavy email-style online task before the loop.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Amazon
Write a query that returns all neighborhoods that have 0 users.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Customer Orders | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Closest SAT Scores | |
| Subscription Overlap | |
| Month Over Month | |
| Button AB Test | |
| Top Three Salaries | |
| Prime to N | |
| Monthly Customer Report | |
| Paired Products | |
| Upsell Transactions | |
| Top 3 Users | |
| Recurring Character | |
| Longest Streak Users | |
| Jars and Coins | |
| Compute Deviation | |
| Download Facts | |
| Experiment Validity | |
| Weekly Aggregation | |
| Bagging vs Boosting | |
| Average Quantity | |
| Daily Retention Summary | |
| Post Composer Drop | |
| Random SQL Sample | |
| Manager Team Sizes | |
| Size of Joins | |
| Cumulative Reset |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report either an online assessment or an early recruiter, manager, or phone conversation. Assessments were described as work-sample, work-style, SQL, numerical, verbal, or coding exercises, so prepare for an initial skills or scenario-based screen rather than assuming one fixed format.
Candidates report SQL interviews ranging from basic query discussion to live work with joins, CTEs, subqueries, and window functions. Some also reported Excel exercises, SQL-plus-Excel tests, or a business case. Explain your logic aloud and connect the analysis to the underlying business question.
Multiple candidates report situational questions tied to Amazon Leadership Principles, including deadlines, customer or stakeholder issues, ownership, and difficult decisions. Interviewers may probe project timelines and individual contributions, so use concrete STAR examples and retain enough detail for follow-up questions.
Candidates report multi-interviewer loops or panels after earlier rounds; some included a Bar Raiser, further behavioral discussion, or another technical component. Expect the mix to vary, and be prepared to revisit both your project history and analytical reasoning in separate conversations.
