Decoding the Amazon Software Engineer Interview: What You Need to Know

What you’ll get from this post

By the time you finish this article you will know exactly how Amazon’s Leadership Principles shape both behavioral and technical interviews, how to structure answers that resonate with interviewers (including Bar Raisers), and a concrete, prioritized prep plan you can use in the next 30 days to improve your odds significantly.

Sound ambitious? It should. Amazon hires for culture fit as much as for technical skill. Aligning both is the fastest path to success.

Quick overview: The interview flow for software engineers

Recruiter screen - role fit, basic background, salary and logistics.
Technical phone screen or online coding - timed problem solving (usually 45–60 minutes). Expect a shared editor (CoderPad, HackerRank, or similar).
Onsite loop (or virtual loop) - 4–6 interviews: coding, design (system or component), and 2+ behavioral/leadership principle interviews. One interviewer is often a Bar Raiser.
Offer and debrief.

Interviews vary by level (SDE I/II/III, Senior, Principal) but the core remains: coding + design + leadership principles.

Why Amazon’s Leadership Principles (LPs) matter - they shape every question

Amazon’s Leadership Principles are not a checklist only for managers. They are the lens interviewers use to evaluate every answer. Expect your code problems, system designs, and behavioral stories to be probed for evidence of these principles.

Key consequences:

Behavioral interviews use STAR (Situation, Task, Action, Result) to map answers directly to LPs.
Technical interviews probe tradeoffs that reveal principles like “Think Big,” “Dive Deep,” “Deliver Results,” and “Bias for Action.”
The Bar Raiser evaluates whether you raise the hiring bar - often by pushing on edge cases, ownership, quality, and long-term thinking.

Amazon’s official list of principles is here: https://www.amazon.jobs/en/principles

The leadership principles you’ll hear most often (and how they show up)

Customer Obsession - Ask: how did this help customers? Did you gather customer feedback? Measurable impact?
Ownership - Did you own the end-to-end outcome, including areas outside your job description?
Invent and Simplify / Think Big - Did you create a scalable solution or a novel approach?
Bias for Action - Did you make a timely decision under uncertainty?
Dive Deep - Can you explain implementation details and tradeoffs?
Deliver Results - What was the outcome, with metrics?
Hire and Develop the Best - Mentoring, hiring decisions, raising standards.

Newer principles like “Strive to be Earth’s Best Employer” and “Success and Scale Bring Broad Responsibility” may also appear in behavioral probes for senior roles.

How interviewers map questions to LPs - practical examples

Coding problem: Interviewer asks why you chose X algorithm. They’re probing Dive Deep and Insist on the Highest Standards.
System design: Interviewer asks how you’d scale from 10k to 1M users. They’re testing Think Big, Dive Deep, and Ownership.
Behavioral: “Tell me about a time you disagreed with your manager.” They want to see Earn Trust, Have Backbone; Disagree and Commit, and Deliver Results.

If you answer technically but never tie the work back to customers, ownership, or outcomes, you miss the core evaluation axis.

The STAR framework - tuned for Amazon

Amazon expects crisp STAR answers. But there’s a tweak: always end with a metric and a short reflection tying the story to the LP.

Structure:

Situation - 1–2 sentences. Context only.
Task - 1 sentence. What was needed.
Action - 3–6 sentences. Focus on your specific contributions and decision rationale. Call out tradeoffs and data used.
Result - 1–2 sentences. Concrete metrics, timeline, and what you learned. Tie to the principle.

Example transition sentence at the end: “This demonstrates Ownership because I took responsibility for the whole feature from spec to monitoring, and it delivered a 24% reduction in error rate within six weeks.”

Two sample STAR answers you can adapt

Note: keep these as templates. Your own stories should be specific and quantified.

Ownership + Deliver Results

Situation: Our team was missing a critical SLA - API latency spiked under load.
Task: I was asked to lead the effort to diagnose and fix the incident.
Action: I collected metrics, wrote a reproducer for the hot path, identified a lock contention issue, proposed a lock-free queue for the hot path, built and tested the change in a canary, and rolled it to 10% then 100% while monitoring health.
Result: Latency 95th percentile dropped from 220ms to 60ms; error rate fell 30% within two weeks. This demonstrates Ownership and Insist on the Highest Standards because I took the full roll-out responsibility and instrumented metrics to prove impact.

Customer Obsession + Think Big

Situation: Users kept asking for faster search results in our admin console.
Task: Improve perceived search latency for power users.
Action: I interviewed 12 power users, discovered most searches were repeated with small variations, implemented client-side caching with a delta query protocol, and added telemetry to measure cache hit patterns.
Result: Perceived response time dropped by 50% for power users, engagement for target flows rose 18%. This shows Customer Obsession - we started with user interviews and prioritized the solution to deliver measurable user value.

Technical round prep - what to prioritize

Coding fluency (60–70% of your prep time if you’re early/mid-career)
- Practice medium-hard problems (arrays, strings, trees, graphs, dynamic programming). Use timed mocks.
- Talk through tradeoffs: why O(n log n) vs O(n) matters here.
- Tools: https://leetcode.com, https://www.hackerrank.com.
System design (20–30% of your prep time for senior roles)
- Sketch end-to-end systems, data flow, storage, caching, monitoring, failure modes.
- Practice scale questions and explicitly call out tradeoffs and metrics (latency, throughput, cost).
- Resources: “Designing Data-Intensive Applications” and community guides.
Readability and tests
- Write clean code and talk about how you’d test it. Amazon values operational excellence.
Mock interviews
- Do live mocks where you code out loud and use STAR for behavioral questions. Services: https://interviewing.io, peer mocks, or paid coaches.

Behavioral prep - concrete mapping exercise

Do this for 8–10 stories:

Pick a story for each of the top LPs (Customer Obsession, Ownership, Deliver Results, Dive Deep, Invent and Simplify).
For each story, write a 6–8 sentence STAR and add a one-line metric.
Map auxiliary LPs the story also demonstrates (e.g., a design story can demonstrate Think Big + Dive Deep + Ownership).

Example mapping table (informal):

Story: Reduced latency - Ownership, Insist on Highest Standards, Dive Deep, Deliver Results.
Story: Mentored junior engineers - Hire and Develop the Best, Earn Trust.

Repeat until you can give each story in 90–120 seconds and answer follow-ups about tradeoffs and details.

How to handle the Bar Raiser

Understand the role: Bar Raisers protect hiring standards and focus on long-term fit, not just near-term delivery.
What they’ll probe: edge cases, ownership, long-term thinking, and how you handle ambiguity.
Tactics: When they push you to defend a decision, don’t get defensive. Show tradeoff thinking, ask clarifying questions, and be explicit about how your decision scales or introduces risk.

Phrase example: “Great point - the tradeoff here is X vs Y. I chose X because of Z (impact, cost, time-to-market), but if the scale crosses threshold Q, I’d switch to Y and here’s how I’d detect that with metrics…”

Common pitfalls and how to avoid them

Story-only answers: Interviewers want impact and metrics, not just a list of activities. Fix: End stories with numbers and the LP they demonstrate.
Over-verbosity in Situation/Task: Trim context. Action + Result are what matter.
No reflection: Amazon values learning. Always add what you’d do differently.
Ignoring customers: Even technical stories should tie back to user or business impact.
Weak testability: For coding, ensure your solution handles edge cases and is verbally tested.

30-day action plan (prioritized)

Week 1

Gather 8–10 behavioral stories and write STARs. Map each to 2–3 LPs.
Do 5 timed coding problems (45–60 minutes each), focusing on medium level.

Week 2

10 more coding problems including 2 hard problems.
One mock onsite loop (3 interviews): coding + behavioral + design.

Week 3

System design practice: 3 full designs with tradeoffs and metrics.
Polish STARs; add metrics and reflections. Rehearse out loud.

Week 4

Daily timed coding (2 problems/day), weekly full mock loop with different interviewers.
Review code and stories; make final adjustments.

Example “probeable” follow-ups and how to answer them

“Why not do X instead?” - Explain the tradeoffs concretely. Mention constraints (latency, cost, dev time) and how you’d measure success.
“What did you monitor after rollout?” - Give specific metrics, SLOs, dashboards, and alert thresholds.
“Did you involve other teams?” - Describe cross-team communication and escalation steps.

Answer strategy: short answer + one-sentence justification + one metric or monitoring step.

Final tips - how to be remembered

Quantify everything. Numbers stick.
Use the LP language naturally. Say “This shows Ownership” or “I prioritized customer experience” sparingly and only when it fits.
Stay calm under pushback. Bar Raisers want to see composure.
Ask insightful questions at the end that show long-term thinking (e.g., “How does the team measure long-term customer retention for this product?”).

Resources

Amazon Leadership Principles: https://www.amazon.jobs/en/principles
LeetCode (practice): https://leetcode.com
Interviewing.io (mock interviews): https://interviewing.io
“Designing Data-Intensive Applications” (book): https://dataintensive.net

Parting thought

Preparing for Amazon is not just about solving the next algorithm problem. It’s about showing you will do the right thing for customers and the company - repeatedly, and at scale. Structure your stories, quantify your impact, and practice the tradeoff conversations. Do that, and you don’t just answer interview questions - you demonstrate the leadership Amazon hires for.