Amazon Leadership Principles Interview Questions (2026)

Amazon Leadership Principles interview questions are behavioral questions mapped to Amazon's 16 Leadership Principles (LPs), such as "Tell me about a time you went above and beyond for a customer" for Customer Obsession. Nearly every round in an Amazon loop, including coding and system design, includes one or two of these questions, and the Bar Raiser uses your answers to decide whether you raise the hiring bar. The winning strategy is a bank of eight to twelve specific, quantified STAR stories, each tagged to the principles it proves.

Key Takeaways

• Amazon has 16 Leadership Principles, and interviewers are assigned specific ones to assess in each round of the loop.
• Expect LP questions in almost every interview, typically 15 to 25 minutes per round, not just in a dedicated behavioral round.
• The Bar Raiser is an interviewer from outside the hiring team with veto power, focused on long-term hiring quality.
• Strong answers are specific, use "I" for your actions, include data, and survive three or four layers of follow-up questions.
• Build a story bank of eight to twelve stories mapped to multiple LPs so you never repeat a story within one loop.

What Are the Amazon Leadership Principles?

The Amazon Leadership Principles are 16 published statements that describe how Amazon expects employees to think and act. They are used in hiring, performance reviews, promotion documents, and day-to-day decision making. In interviews, they are the scoring rubric for behavioral questions.

Amazon published 14 principles for years and added two in 2021: Strive to be Earth's Best Employer and Success and Scale Bring Broad Responsibility. The full list as of 2026:

For the full round structure of a software engineering loop, including the online assessment, coding rounds, and system design, see our Amazon technical interview process guide.

How Are Leadership Principles Used in the Amazon Interview Loop?

A typical Amazon SDE loop has four to five interviews after the online assessment or phone screen. Before the loop, the recruiter or hiring manager assigns two or three Leadership Principles to each interviewer. Each interviewer then asks LP questions targeting their assigned principles, usually at the start of the round, before moving to coding or design.

After the loop, every interviewer writes detailed feedback with evidence for each principle they assessed, and the group meets for a debrief led by the Bar Raiser. A single weak signal on a core principle like Ownership or Customer Obsession can outweigh a solid coding performance, especially at SDE II and above.

Two consequences follow:

1. Behavioral preparation is not optional. It affects every round.
2. Story diversity matters. If two interviewers hear the same story, the second one gets little new evidence, which weakens your packet.

What Does an Amazon Bar Raiser Look For?

A Bar Raiser is an experienced Amazon employee, trained specifically for the role, who sits on hiring loops outside their own organization. They have no stake in filling the open role quickly, which is the point. Their mandate is long-term hiring quality: every new hire should be better than at least half of the people currently at that level.

In practice, Bar Raisers look for:

• Specificity. Concrete situations with names of systems, real numbers, and dates, rather than hypotheticals or composites.
• Personal contribution. What you did, decided, and wrote, separated clearly from what the team did.
• Depth under follow-up. They often ask "why" and "how did you know" three or four times. Stories that fall apart at the second follow-up score poorly.
• Self-awareness. Honest discussion of mistakes, what you learned, and what you would do differently.
• Level calibration. Whether the scope, ambiguity, and impact of your stories match the level you are interviewing for.

The Bar Raiser may also ask a technical question, and they lead the debrief. They can veto a hire even if the hiring manager wants to proceed.

The Most Common Questions for Each Leadership Principle

Below are the questions that appear most often in candidate reports for each principle, followed by what a strong answer needs to show. Expect variations in wording.

Customer Obsession

• Tell me about a time you went above and beyond for a customer.
• Describe a time you had to balance customer needs against business or technical constraints.
• Tell me about a time you used customer feedback to change a product or technical decision.

A strong answer names a real customer (internal or external), describes how you learned what they needed, and shows a decision that put their outcome ahead of convenience.

Ownership

• Tell me about a time you took on something outside your area of responsibility.
• Describe a time you made a decision that sacrificed short-term gains for long-term results.
• Tell me about a time a project failed and you were responsible.

Show that you acted without being asked and stayed accountable for the long-term outcome, including on-call, cleanup, and follow-through.

Invent and Simplify

• Tell me about the most innovative solution you have built.
• Describe a time you simplified a complex process or system.

Quantify the simplification: lines of code removed, steps eliminated, latency reduced, on-call pages cut.

Are Right, A Lot

• Tell me about a time you made a decision with incomplete data.
• Describe a time you were wrong. How did you find out and what did you do?

Show how you sought disconfirming evidence and changed your mind when the data required it.

Learn and Be Curious

• Tell me about a time you learned a new technology quickly to solve a problem.
• What is something you learned recently that changed how you work?

Hire and Develop the Best

• Tell me about a time you mentored someone. What was the outcome?
• Describe a time you gave difficult feedback to a peer.

Insist on the Highest Standards

• Tell me about a time you refused to compromise on quality.
• Describe a time you raised the bar for your team's engineering practices.

Concrete mechanisms win here: a new test suite, a review checklist, a launch readiness standard, an operational metric you introduced.

Think Big

• Tell me about a time you proposed a bold idea or long-term vision.
• Describe a project where you expanded the scope beyond what was originally asked.

Bias for Action

• Tell me about a time you made a decision quickly without all the information.
• Describe a time you took a calculated risk.

Explain why the decision was reversible, how you limited the downside, and what you would have done if you were wrong.

Frugality

• Tell me about a time you delivered a result with limited resources.
• Describe a time you reduced cost, such as infrastructure spend.

Earn Trust

• Tell me about a time you had a conflict with a coworker. How did you resolve it?
• Describe a time you admitted a mistake to your team or manager.
• Tell me about a time you received critical feedback.

Dive Deep

• Tell me about a time you used data to find the root cause of a problem.
• Describe a time a metric looked fine but something was actually wrong.

Expect the most aggressive follow-ups on this principle. Know your numbers, query names, dashboards, and the exact sequence of the investigation.

Have Backbone; Disagree and Commit

• Tell me about a time you disagreed with your manager or a senior engineer.
• Describe a time you committed to a decision you disagreed with.

Show that you disagreed with data, escalated respectfully, and then committed fully once a decision was made, without sabotaging it.

Deliver Results

• Tell me about a time you delivered a project under a tight deadline.
• Describe a time you faced a major obstacle and still hit your goal.

Strive to be Earth's Best Employer

• Tell me about a time you made your team a better place to work.
• Describe how you supported a teammate who was struggling.

Success and Scale Bring Broad Responsibility

• Tell me about a time you considered the broader impact of a technical decision, such as on users, privacy, accessibility, or the environment.
• Describe a time you identified an unintended consequence of something you built.

For a broader bank of behavioral prompts beyond Amazon, see our list of the top 50 behavioral interview questions with STAR answers.

How to Structure a Strong STAR Answer for Amazon

STAR stands for Situation, Task, Action, Result. Amazon interviewers are trained on it, and their feedback templates expect it. The most common mistake is spending too long on the Situation and too little on the Action.

A reliable time split for a two to three minute answer:

Sample strong answer structure: Dive Deep

Question: Tell me about a time you used data to find the root cause of a problem.

1. Situation: "Our checkout service's p99 latency rose from roughly 300 ms to over 1 second after a routine deploy, and the dashboards showed no obvious errors."
2. Task: "I was the on-call engineer and owned getting latency back within our SLA."
3. Action: Walk through the investigation step by step. Rolling back did not fully fix it. You broke down latency by endpoint and region, found the regression was isolated to one dependency call, pulled traces, noticed a connection pool exhaustion pattern, and traced it to a config change that reduced pool size. Explain the hypotheses you ruled out and why.
4. Result: "p99 returned to baseline within four hours. I added a pool saturation alarm and a config validation check, and we had no repeat incidents over the next two quarters."

Then prepare for follow-ups: "How did you know it was the pool and not the database?" "Who else did you involve?" "What would you do differently?"

Sample strong answer structure: Have Backbone; Disagree and Commit

1. Situation: A senior engineer proposed migrating a service to a new datastore within one quarter.
2. Task: You were responsible for the migration plan and believed the timeline risked data integrity.
3. Action: You gathered evidence such as a dual-write prototype and failure-mode analysis, wrote a short document with two alternatives, raised it in design review, and escalated respectfully. When leadership chose a compromise timeline, you committed fully and owned the risk mitigations.
4. Result: The migration shipped two weeks later than the original target with zero data loss, and the dual-write validation became the team's standard migration pattern.

How to Build an Amazon LP Story Bank

A story bank is a short list of your best career stories, each tagged with the Leadership Principles it demonstrates. Building one before the loop is the single highest-leverage preparation step for Amazon behavioral rounds.

1. List 12 to 15 candidate stories from the last three to five years: launches, incidents, conflicts, failures, migrations, mentoring, cost savings, and decisions under ambiguity.
2. Keep the best 8 to 12. Prefer recent stories with clear personal ownership and measurable outcomes. At SDE II and above, prefer stories with cross-team scope.
3. Tag each story with two or three principles. Make sure every one of the 16 has at least one story, and the most commonly asked ones have two or three.
4. Write each story as bullets, not a script. Include the numbers, system names, and the two or three decisions that mattered.
5. Prepare one failure story and one conflict story that you can tell without blaming anyone. Both are near-certain to come up.
6. Rehearse out loud with follow-ups. Have a friend ask "why" four times. Fix any story that collapses.
7. Track usage during the loop. After each round, note which stories you used so you do not repeat them with the next interviewer.

A simple story-bank matrix keeps this manageable:

Replace the examples with your own. The point is coverage: every principle should map to at least one story you can tell with confidence.

Common Mistakes in Amazon Behavioral Interviews

• Hypothetical answers. "I would..." instead of "I did..." is an immediate weak signal. Amazon asks about past behavior on purpose.
• "We" everywhere. Interviewers cannot credit you for what the team did.
• No numbers. Results without metrics read as unverified. Approximate numbers are fine if you are honest that they are approximate.
• Polished but shallow stories. Over-rehearsed answers that cannot handle follow-ups score worse than slightly rough answers with real depth.
• Blaming others in conflict and failure stories, which fails Earn Trust.
• Stories below level. An SDE II candidate who only tells stories about individual bug fixes signals SDE I scope.
• Reading AI-generated answers verbatim. Interviewers increasingly recognize generic phrasing. If you are curious how that plays out, see whether interviewers can tell if you use ChatGPT.

Our behavioral interview guide for software engineers covers delivery techniques that apply beyond Amazon, and once you get the offer, our salary negotiation guide explains how Amazon's back-loaded vesting and sign-on bonuses work.

How to Practice in the Final Week

1. Re-read the official Leadership Principles text on Amazon's site, and note the exact wording of each.
2. Do two timed mock sessions in which a partner picks random principles and asks three follow-ups per story.
3. Record yourself. Cut any answer that runs over three minutes before the follow-ups start.
4. Prepare two or three thoughtful questions for each interviewer about how their team applies a principle in practice, such as how they balance Bias for Action against Insist on the Highest Standards.
5. Sleep before the loop. Five back-to-back rounds of behavioral and technical questions are draining, and late-loop answers are where detail slips.