INTERVIEW GUIDE

Amazon Business Analyst Interview: Questions & Process

Amazon's Business Analyst interview is half technical (SQL, metrics, building a recommendation from messy data) and half Leadership Principles. Almost every behavioral question maps to an LP like Dive Deep or Customer Obsession, and a Bar Raiser sits in your loop to keep the bar high. Expect a recruiter screen, an online assessment, and a 4-5 interviewer virtual loop.

The interview process

1. Recruiter screen — ~30 min call

Tests: background, SQL comfort, why Amazon, and a primer on the Leadership Principles

2. Online assessment — 60-90 min, timed

Tests: SQL queries, a data-analysis/work-style simulation, and sometimes basic stats or Excel logic

3. Technical phone screen — 45-60 min

Tests: live SQL (joins, aggregations, window functions) plus a metrics or business judgment question

4. Virtual onsite loop — 4-5 back-to-back 45-60 min interviews

Tests: deeper SQL, metric design, a business case, and 2-3 LP-driven behavioral rounds

5. Bar Raiser — embedded in the loop

Tests: a trained interviewer from outside the team probing depth, ownership, and whether you raise the hiring bar

Questions you're likely to get

Technical

Write a SQL query to find the top 10 products by revenue in the last 30 days, excluding cancelled orders.
Using a window function, rank customers by spend within each region.
A daily metric dropped 20% overnight. Walk me through the SQL and logic you'd use to find the cause.
How would you measure whether a new feature actually improved customer retention?
Explain the difference between a LEFT JOIN and an INNER JOIN with a real example where it changes the answer.

Role-specific

Define the one metric you'd use to track the health of a delivery operation, and defend it.
You're handed a dirty dataset with duplicates and nulls. How do you make it trustworthy before analyzing it?
A stakeholder wants a dashboard by Friday but the data is ambiguous. What do you do?

Behavioral

Tell me about a time you dove deep into data and found a root cause others missed. (Dive Deep)
Describe a time you made a decision with incomplete information. (Bias for Action)
Tell me about a time you disagreed with a manager or stakeholder and what happened. (Have Backbone; Disagree and Commit)
Give an example of when you put the customer first even though it was harder for you. (Customer Obsession)
Tell me about a goal you set that was higher than what was asked of you. (Insist on the Highest Standards)

How to answer (worked examples)

Tell me about a time you dove deep into data and found a root cause others missed.

STAR, but front-load the numbers. Situation: the metric or anomaly and why it mattered. Task: what you owned. Action: the specific queries, segments, and hypotheses you tested (name the columns, the cohorts, the joins) — this is where Dive Deep is scored. Result: the quantified finding and the decision it changed. Amazon interviewers will keep asking 'how did you know?' so have the second and third layer of detail ready.

A daily metric dropped 20% overnight.

Don't jump to a query. First clarify the metric definition and timeframe, then split the problem: is it real or an instrumentation/data-pipeline issue? Segment by dimension (region, device, new vs returning) to localize it, form a hypothesis, and only then write the SQL to confirm. Narrate your reasoning out loud — they're testing structure, not a memorized answer.

Define the one metric you'd use to track operation health.

Pick a single primary metric, state exactly how it's calculated, and explain why it beats the obvious alternatives. Then name one counter-metric so you don't game it. Tie it back to the customer — Amazon rewards answers that connect operational numbers to customer experience.

What Amazon looks for

Fluent, correct SQL written under light time pressure — including window functions and multi-table joins
Structured problem-solving: you decompose ambiguous questions before touching data
Real depth on metrics — you can define one precisely and defend the tradeoffs
Stories that map cleanly to Leadership Principles with quantified results
Ownership: you drove the analysis to a decision, not just a chart
Red flag: vague behavioral answers ('we' instead of 'I') or hand-waving when asked 'how did you know?'

FAQ

How important are the Leadership Principles, really?

Central. Most behavioral questions are LP probes, and your loop debrief is organized around them. Have 6-8 detailed stories prepared, each tagged to one or two LPs, with metrics you can defend under follow-up.

How much SQL do I need?

Solid intermediate SQL: joins, GROUP BY, subqueries, CASE, and window functions like ROW_NUMBER and RANK. You don't need exotic syntax, but you should write correct queries quickly while explaining your logic.

What is a Bar Raiser?

A trained interviewer from outside the hiring team whose job is to protect Amazon's hiring bar. They have effective veto power, so treat every round as equally important — you won't know which one is the Bar Raiser.

Is there an online assessment for new grads?

Often yes — a timed SQL and work-simulation assessment before the loop. Practice writing SQL by hand without an autocomplete crutch so the timer doesn't rattle you.

How long does the process take?

Typically 3-6 weeks from recruiter screen to decision, though timing varies by team and hiring season.

Practice the LP stories out loud before your loop

The hardest part of an Amazon loop isn't the SQL — it's delivering tight, metric-backed Leadership Principle stories under rapid-fire follow-ups. Rehearse this exact interview with OfferLoop's realtime voice coach so 'Dive Deep' feels automatic, not improvised.

Practice this interview out loud →

All Amazon interviews →Amazon Software Engineer interview →Amazon Data Analyst interview →Amazon Data Scientist interview →Amazon Data Engineer interview →

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Interview formats vary by team, level and year, and this guide is compiled from general knowledge of publicly discussed hiring processes — treat it as preparation material, not an official description of Amazon's current process.