Amazon Business Analyst candidates report an online assessment, a SQL or Excel screen, and a loop where Leadership Principles stories carry most of the weight. Formats vary by team, so prepare both behavioral and technical material.
$109K
Avg. Base Comp
$140K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Amazon Business Analyst interviews center on Leadership Principles stories, usually backed by a SQL, Excel or business-case check. Several candidates describe an online assessment first. One describes it as a work simulation that gave a useful preview of the day-to-day job. Later stages reported include a phone interview with a manager and an analyst, a technical screen, and a loop of interviews. One candidate had four Zoom interviews of about 45 minutes each, including a Bar Raiser.
Behavioral depth is the most repeated theme. Candidates were pushed past polished summaries into follow-ups about what they personally did, why they made certain decisions and how they handled ambiguity. Reported prompts include why Amazon, a difficult customer, trusting your gut without all the data, and handling multiple payment requests under a tight deadline. Others include a time you innovated and creating something a stakeholder didn't know they needed. Have several distinct STAR stories ready, since one candidate was told to expect two or three principle-based questions.
Technical depth varies by team. One candidate reports an hour-long SQL round on a whiteboard that included window functions. Another had an hour-long screen mixing SQL with behavioral questions. Others saw an Excel task, a business case with on-the-spot profit and percentage calculations, a financial-modeling-style case, or a question about analytics tools in the market. Outcomes were mixed, with offers and rejections both reported, so rehearse explaining your reasoning aloud. Scheduling speed and recruiter communication also varied widely between candidates.
Synthesized from 83 candidate reports by our editorial team.
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Real interview reports from people who went through the Amazon process.
The thing that surprised me most about interviewing for a Business Analyst II role on Amazon Logistics, OTR Planning, was how little the original question mattered compared to the follow-ups. The process was long: an online assessment, a 60-minute phone screen, a recruiter preparation call, and then a five-interview virtual loop spread across three days.
The online assessment had a substantial SQL section, around 110 minutes, plus a short Workstyle assessment. I never hit a separate live-coding round in the loop itself, but SQL, data modeling, KPI logic, data validation, dashboards, and analytical problem solving kept coming up throughout. The phone screen leaned on BI experience, KPI investigation, requirements gathering, SQL and data pipelines, and Leadership Principles. I was asked to walk through a time a KPI didn't reconcile with source data: how I traced the issue through the pipeline, validated the root cause, and worked with stakeholders to fix it.
The recruiter prep call was genuinely useful. They stressed saying "I" instead of "we," defending every metric, keeping Situation, Task, Action, and Result clearly separated, and being ready for several levels of follow-up on the same story. That turned out to be accurate. The five loop interviews covered Business Intelligence and Problem Solving alongside Leadership Principles like Customer Obsession, Dive Deep, Ownership, Business Acumen, Deliver Results, Invent and Simplify, Think Big, Earn Trust, and Learn and Be Curious, plus a GenAI Fluency discussion. Interviewers pushed for exact baselines, calculations, why I chose an approach, which alternatives I rejected, how I validated the result, what I did versus the team, how stakeholders reacted, and what I'd change. When I described an automation project, I was asked why SQL plus Power BI rather than improving Excel with VBA or building something larger in the cloud. In another interview I had to explain how I measured adoption of a centralized reporting framework and how I handled teams that wanted to stay in Excel.
The GenAI conversation went deeper than "have you used ChatGPT." I talked about approved enterprise tools with sanitized, non-sensitive data, structured prompting, human review, stakeholder validation, source traceability, and using AI for first drafts rather than final business decisions.
The interviewer experience was uneven. Most were friendly and professional, but two interviews felt abrupt, with almost no introduction before going straight into evaluation. One interviewer told me afterward that I speak too quickly, which can make a good story harder to follow. I completed all five interviews and then got a rejection email with no detailed feedback.
Prep tip from this candidate
Know the numbers behind every story: baseline, final figure, calculation, data source, validation method, and the alternatives you rejected, since interviewers drill several levels deep. Also prepare a clear answer on responsible GenAI use (sanitized data, human review, source traceability) and slow your pace when telling stories.
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| Question | |
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| 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 | |
| Retailer Data Warehouse | |
| Experiment Validity | |
| Download Facts | |
| Weekly Aggregation | |
| Bagging vs Boosting | |
| Daily Retention Summary | |
| Average Quantity | |
| Size of Joins | |
| Post Composer Drop | |
| Random SQL Sample | |
| Manager Team Sizes |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report an initial online assessment. One describes a work simulation that previewed the day-to-day job, and others mention receiving an assessment invite about two weeks after applying. Plan for the format to vary by team and role.
Several candidates describe an early screen, such as a phone interview with a manager and an analyst or a general screening interview. Reported content includes a walkthrough of a recent project, scenario questions and how your past experience fits the role. Expect to explain your past work concisely.
Candidates report live SQL, including whiteboard problems with window functions and an hour-long screen mixing SQL and behavioral questions. Others describe an Excel task, a business case with quick profit and percentage calculations, or a financial-modeling-style case. The technical depth typically depends on the team.
Most candidates describe a loop of several interviews built around STAR answers tied to Leadership Principles. One candidate had four Zoom interviews of about 45 minutes each, including a Bar Raiser. Follow-up questions probe your own contribution, decisions and judgment.
Candidates report both offers and rejections. Some describe quick follow-up after the interview, while others report slow scheduling, slow results or recruiter communication gaps. Build in patience and keep your STAR stories ready while you wait.
