Amazon Leadership Principles Interview: The Complete Guide

Amazon Interviews Are Not Like Other FAANG Interviews

Every big tech company says they care about culture. Amazon is the only one that has turned it into a formal scoring system. When you interview at Amazon, each interviewer is assigned exactly two Leadership Principles (LPs) to evaluate you on. They ask behavioral questions designed to probe those specific LPs, and their written feedback maps directly to them. Your packet goes to a hiring committee, and the decision hinges on whether you demonstrated enough LPs at the target level.

This means Amazon's behavioral rounds are simultaneously the most predictable and the most punishing of any FAANG company. Predictable because the 16 LPs are published on Amazon's website. Punishing because interviewers are trained to dig deep into your stories with relentless follow-up questions, and a vague answer on a single LP can tank your entire loop.

I've been on both sides of Amazon interviews. What follows is what I wish someone had told me before my first loop.

The 16 Leadership Principles: What Actually Gets Tested

Amazon has 16 Leadership Principles. All 16 are real and used in evaluation. But in a typical interview loop of 4-5 rounds, you'll be tested on 8-10 of them. Some come up in nearly every loop. Others are reserved for specific levels or roles.

If you're interviewing for SDE I or SDE II, focus your preparation on the top 6 in the table above. For senior and principal roles, expect questions on all 16, with emphasis on Hire and Develop the Best and Think Big.

The Six LPs You Must Nail

Customer Obsession

Amazon's definition says "leaders start with the customer and work backwards." What it really means in an interview: can you show that your decisions were driven by user impact, not technical elegance or your own preferences?

Interviewers want to hear stories where you chose a worse technical solution because it was better for the customer, where you pushed back on a product decision because it would hurt users, or where you went beyond the spec to solve a customer problem nobody asked you to solve.

Example questions:

• Tell me about a time you made a decision that prioritized the customer over a business or technical goal.
• Describe a situation where you had to advocate for the customer when it was unpopular.
• Give me an example of when you used customer feedback to drive a change.

Sample STAR answer:

Situation: "I was a backend engineer on a fintech app. Our checkout flow had a 12% drop-off rate at the payment confirmation step. Product wanted to add upsell prompts at that step to increase average order value."

Task: "I was responsible for implementing the upsell feature, but I believed adding friction at the highest-abandonment step would hurt overall revenue, not help it."

Action: "I pulled our analytics data and built a model showing that even a 2% increase in drop-off at that step would cost more revenue than the projected upsell gains. I presented this to the PM and the head of product with a counterproposal: move the upsell to the post-purchase confirmation page instead. I ran a two-week A/B test of both approaches with 10% of traffic."

Result: "The post-purchase upsell generated 80% of the revenue of the checkout upsell with zero impact on conversion. Checkout drop-off actually decreased by 1.5% because we simplified the flow. The PM adopted my approach for the full rollout, and it became the template for how we evaluated all future checkout changes."

Ownership

This is not about being a workaholic. It's about scope of concern. Amazon wants people who see a problem outside their team's domain and fix it anyway, who take responsibility for outcomes rather than just outputs, and who don't wait to be told what to do.

The strongest Ownership stories involve you stepping outside your job description. You noticed something broken, you fixed it, and you didn't need permission.

Example questions:

• Tell me about a time you took on something outside your area of responsibility.
• Describe a situation where you saw a problem that no one was addressing and took the initiative to solve it.
• Give me an example of a time you sacrificed short-term goals for long-term value.

Bias for Action

Amazon explicitly values speed over perfection for reversible decisions. In an interview, this LP probes whether you can distinguish between one-way doors (irreversible decisions that need careful analysis) and two-way doors (reversible decisions that should be made quickly).

The trap here is telling a story about rushing and making a mess. The ideal story shows you recognizing that a decision was reversible, moving fast, and getting a result. Bonus points if you also show awareness of when to slow down.

Example questions:

• Tell me about a time you had to make a decision without complete information.
• Describe a situation where you took a calculated risk.
• Give me an example of when you moved quickly on something important.

Sample STAR answer:

Situation: "I was on a platform team at an e-commerce company. On a Friday afternoon, we noticed our recommendation engine was returning stale results for about 15% of users. The root cause wasn't immediately clear, but the impact on click-through rates was already measurable."

Task: "I needed to decide whether to spend the weekend investigating the root cause or deploy a quick mitigation that would reduce the blast radius while we investigated properly on Monday."

Action: "I assessed the risk: the mitigation was a cache TTL reduction from 24 hours to 2 hours. It would increase our cache miss rate and put more load on the recommendation service, but our headroom was sufficient. I made the call within 30 minutes, deployed the change, monitored it for an hour, and confirmed the stale results dropped to under 1%. I documented what I'd done and why in our incident channel so the on-call engineer had full context."

Result: "The mitigation held through the weekend. On Monday, the root cause turned out to be a race condition in our cache invalidation logic, which took two days to fix properly. By acting fast on Friday, we avoided two days of degraded recommendations affecting roughly 200,000 user sessions. My manager cited this as an example of good two-way-door thinking in our next team retrospective."

Dive Deep

This LP tests whether you stay connected to the details or only operate at the strategy level. Amazon is suspicious of managers and senior engineers who can't explain how their own systems work under the hood.

Good Dive Deep stories involve you personally investigating a production issue, auditing data to find a discrepancy nobody else noticed, or questioning a metric that looked correct on the surface.

Example questions:

• Tell me about a time you found a significant problem by looking at the details.
• Describe a situation where everyone accepted a result but you dug deeper.
• Give me an example of when data told one story but the reality was different.

Deliver Results

Straightforward but often answered poorly. This LP is about outcomes, not effort. Amazon doesn't want to hear about how hard you worked. They want to hear about what you shipped and what impact it had.

The best Deliver Results stories involve overcoming obstacles. Something went wrong, the timeline was at risk, and you found a way to deliver anyway. If your story is "everything went according to plan and we shipped on time," it's too boring for this LP.

Example questions:

• Tell me about a time you delivered a project under challenging conditions.
• Describe a situation where you had to overcome significant obstacles to achieve a goal.
• Give me an example of a time you had to deprioritize to focus on what mattered most.

Earn Trust

This is the interpersonal LP, and it's where many technical candidates stumble. Earn Trust is about being honest even when it's uncomfortable, admitting mistakes, and building credibility through transparency rather than authority.

The strongest Earn Trust stories involve conflict. You disagreed with someone, you handled it with candor and respect, and the relationship was stronger afterward. Stories about admitting you were wrong also score very well here.

Example questions:

• Tell me about a time you had to deliver difficult feedback to a peer or manager.
• Describe a situation where you earned trust from a skeptical stakeholder.
• Give me an example of when you admitted a mistake and how you handled it.

Sample STAR answer:

Situation: "I was leading the migration of our monolith to microservices. Two weeks in, I realized my initial architecture proposal had a fundamental flaw: the service boundaries I'd drawn would create a circular dependency between three services, making independent deployment impossible."

Task: "I needed to address the problem without losing the team's confidence in the migration plan or my technical judgment."

Action: "I wrote up a one-page doc that clearly stated the mistake, explained what I'd gotten wrong in my original reasoning, and proposed a revised architecture with clean dependency boundaries. I presented it at the next team meeting without any hedging. I said, 'I got this wrong, here's why, here's the fix.' I also invited the team's most experienced engineer to co-review the revised boundaries before we moved forward."

Result: "The team adopted the revised architecture without delay. My manager told me afterward that my handling of the situation actually increased the team's trust in the migration. Two junior engineers later told me it changed how they thought about admitting mistakes. We completed the migration on schedule with the revised design."

Amazon's Interview Loop Structure

Understanding the structure removes surprises. Here's what to expect.

Phone Screen (45-60 minutes)

One interviewer. Usually a mix of one coding question (20-30 minutes) and 1-2 behavioral questions mapped to specific LPs. This is a filter round. The bar is lower than the onsite, but you still need to demonstrate clear LP alignment in your behavioral answers. If you're asked "Why Amazon?" here, tie your answer to specific LPs.

Virtual Onsite (4-5 rounds, 45-60 minutes each)

Each round has a designated interviewer assigned 2 Leadership Principles to evaluate. The typical structure for an SDE II loop:

• Round 1: Coding + LPs (e.g., Customer Obsession + Ownership)
• Round 2: Coding + LPs (e.g., Bias for Action + Deliver Results)
• Round 3: System design + LPs (e.g., Dive Deep + Invent and Simplify)
• Round 4: Behavioral deep-dive + LPs (e.g., Earn Trust + Have Backbone; Disagree and Commit)
• Round 5 (Bar Raiser): Mixed format + any LPs the loop is missing coverage on

Every round includes behavioral questions, even the coding and system design rounds. Interviewers typically spend the first 20-25 minutes on behavioral and the remaining time on the technical portion. Some interviewers flip this order. The point is: you cannot skip behavioral prep for any round.

Here's something most candidates don't realize: your interviewers coordinate beforehand. They know which LPs the others are covering. If your Round 1 interviewer didn't get strong enough signal on Customer Obsession, they may flag it, and a later interviewer will ask an additional question to fill the gap. This is why you need more stories prepared than you think you'll need.

The Bar Raiser: Amazon's Secret Weapon

One interviewer in your loop is a Bar Raiser. This person is from a different team and has no stake in whether you get hired. Their job is to protect Amazon's hiring bar. They have veto power: even if the hiring manager and all other interviewers say "hire," the Bar Raiser can single-handedly reject you.

What this means in practice:

• The Bar Raiser's questions tend to be harder and more probing. They'll dig into your stories with more follow-ups than other interviewers.
• They evaluate you against the entire company's bar, not just the team's immediate needs. A team might be desperate to fill a role, but the Bar Raiser doesn't care about that.
• They often cover LPs that other interviewers haven't fully tested. If there's a gap in your LP coverage, the Bar Raiser will fill it.
• You won't know which interviewer is the Bar Raiser. Don't try to guess. Treat every interviewer as if they have veto power, because one of them does.

The Bar Raiser system is genuinely effective. It's why Amazon maintains a relatively consistent hiring bar across thousands of interviewers worldwide. It's also why you can have four strong rounds and still get rejected if your Bar Raiser round was weak.

How do you prepare for the Bar Raiser specifically? You don't. You prepare for every round the same way. But there are a few things worth knowing. Bar Raisers tend to focus heavily on the "why" behind your decisions. They'll accept your story at face value less often than other interviewers. If you say "I decided to use Kafka," expect the follow-up: "What other options did you consider? Why not a simple database queue? What throughput were you actually dealing with?" They want to see the reasoning, not just the outcome. They're also more likely to test LPs like Earn Trust and Have Backbone; Disagree and Commit, because those LPs are harder to fake and reveal more about how you actually operate under pressure.

The Mistakes That Get People Rejected

1. Using "We" Instead of "I"

This is the single most common piece of feedback in Amazon interview debriefs: "Candidate consistently used 'we' and I couldn't determine their individual contribution." Amazon interviewers are trained to notice this. Every time you say "we decided" or "we built," the interviewer internally notes that they can't attribute the action to you.

Use "I" as the subject of most sentences. You can acknowledge your team: "I coordinated with our three frontend engineers to..." But the verb needs to follow "I," not "we." If you need a refresher on structuring individual-focused answers, the STAR method guide covers this in detail.

2. Not Quantifying Results

"The project was successful" is not a result. "We launched the feature and users liked it" is barely better. Amazon interviewers want numbers: revenue impact, latency reduction, user adoption rates, cost savings, time saved. If you don't have exact numbers, estimate. "Reduced page load time by roughly 40%, which we estimated contributed to a 2% increase in daily active users" is far more compelling than "made it faster."

Before your interview, go back through your stories and attach numbers to every result. Even ballpark figures dramatically improve your score.

3. Picking Stories That Don't Map to the LP

When asked about Customer Obsession, don't tell a story about optimizing a CI/CD pipeline unless you can directly connect it to customer impact. When asked about Ownership, don't tell a story where your manager assigned you the task. The story itself might be impressive, but if it doesn't demonstrate the specific LP being tested, it hurts you.

Prepare your story bank with LP tags. For each of your 8-10 stories, note which 2-3 LPs it can demonstrate. When the interviewer signals which LP they're probing, pull the right story. For a comprehensive list of behavioral questions organized by theme, see our top behavioral questions guide.

4. Not Handling Follow-Up Questions Well

Amazon interviewers are trained to probe. After your initial answer, expect 2-4 follow-up questions: "What would you do differently?" "Who disagreed with you?" "What was the customer impact specifically?" "Walk me through the data you used."

These follow-ups are not adversarial. They're the interviewer trying to get enough signal to give you a positive score. Treat them as opportunities, not attacks. If you don't know the answer, say so honestly rather than fabricating.

5. Not Having Enough Stories

You need 8-10 well-prepared stories minimum. Many candidates prepare 4-5 and try to reuse them. Here's the problem: if your Round 2 interviewer asks about a time you failed and you use the same story you just told your Round 1 interviewer, the interviewers will compare notes in the debrief and it looks like you only have one experience to draw from.

Each story should be versatile enough to stretch across 2-3 LPs, but you should never tell the exact same story in two different rounds.

How to Prepare: A Practical Plan

Four weeks is enough if you're deliberate about it.

Week 1: Story Mining. Go through your career and identify 10-12 strong stories. Categorize each by the LPs it demonstrates. Fill out a STAR worksheet for every story. Write them down; don't just think about them.

Week 2: Refine and Pressure-Test. Practice each story out loud. Time yourself. Cut anything over 2 minutes. Get a friend or use Hoppers AI's behavioral mock interviews to simulate LP-specific questioning with follow-ups. The follow-ups are where most people fall apart, and you can only get better at them through practice.

Week 3: Technical Prep. Amazon's coding bar is LeetCode medium to hard. System design focuses on scalable distributed systems. But don't neglect the behavioral component of technical rounds. Practice transitioning smoothly from a behavioral answer to a coding problem within the same 60-minute block.

Week 4: Full Simulations. Do 2-3 full mock loops. Each simulation should be 4 rounds of 45 minutes with different LP pairings. Debrief after each one. By this point your stories should feel natural, not rehearsed.

Throughout all four weeks, read Amazon's LP descriptions on their website and think about how your stories connect. The interviewers use the official descriptions as their rubric, so your language should echo theirs without sounding scripted.

The Amazon-Specific STAR Twist

Standard STAR method works at Amazon, but add one element: after your Result, briefly state which LP your story demonstrates and what you learned. Amazon interviewers appreciate this self-awareness. It shows you understand their framework, not just your own story.

For example, after describing how you pushed back on a product decision to protect the customer: "This experience reinforced my belief that customer trust is earned in moments where the easy path is to stay quiet. I'd rather have an uncomfortable conversation than ship something I know will frustrate users."

That closing sentence screams Customer Obsession and Earn Trust without naming them directly. That's the level of sophistication that separates a "hire" from a "strong hire."

Final Advice

Amazon's interview process is rigorous but fair. The LPs are public. The format is well-documented. There are no trick questions. What trips people up is lack of preparation, not lack of ability.

Three things to remember walking into your loop:

• Every answer is a data point for a specific LP. Know which LP is being tested and tailor your story accordingly.
• The Bar Raiser is looking for reasons to say no. Don't give them ammunition with vague answers, unquantified results, or stories where your role is unclear.
• Amazon values conviction backed by data. Have strong opinions, but show that those opinions were formed through evidence, not ego.

The candidates who do best at Amazon aren't the ones with the most impressive resumes. They're the ones who've done the most deliberate preparation. The LPs are a framework, and frameworks reward people who practice within them.

If you're also interviewing at other FAANG companies, note that the behavioral preparation you do for Amazon transfers well. Google evaluates "Googleyness" through similar behavioral questions, though without the formal LP framework. Our Google interview guide covers the differences. But Amazon is the one company where behavioral answers are weighted equally with technical performance at every level. Treat your LP preparation with the same seriousness you give your coding prep, and you'll be ahead of most candidates walking into the loop.