Amazon's interview process has one feature no other major tech company has: the Leadership Principles (LPs). They're not a formality — they're half the interview. Candidates who treat LP prep as an afterthought fail Amazon loops they would have passed at any other company.
This guide covers the 2026 Amazon SWE loop, how LPs are actually evaluated (not the clichéd STAR advice), the coding and system design bar, and how to prepare for both halves of the interview.
Practice both halves: run Amazon-style behavioral + coding voice mocks at interview-prep.academy — AI asks LP follow-ups like a real bar raiser and scores your communication. Free, no card.
The 2026 Amazon SWE loop: structure
| Round | What's evaluated | Duration |
|---|---|---|
| Online assessment | 2 coding problems + work simulation | 90–120 min |
| Phone screen | 1 coding + 1–2 LP questions | 60 min |
| Virtual on-site: coding x2 | DSA problems + LP questions in each | 60 min each |
| Virtual on-site: system design | Design a large-scale distributed system | 60 min |
| Virtual on-site: bar raiser | Any mix — often heavier LP, sometimes extra coding | 60 min |
| Hiring meeting | Feedback aggregation + level decision | — |
Key Amazon-specific mechanics:
- Bar Raiser: one interviewer is trained specifically to maintain Amazon's hiring bar, independent of the team. They can veto a hire. The bar raiser round is often the hardest.
- Every round has LP: even the coding rounds include 1–3 behavioral questions. You will answer LP questions in 5 of 5 rounds, not just one.
- Written feedback: interviewers must write specific examples mapped to Amazon's LPs. Vague answers don't pass — they have nothing to write.
Part 1: Leadership Principles — how Amazon actually uses them
Amazon has 16 LPs. Interviewers aren't checking boxes — they're looking for evidence that you've internalized these principles through real work.
The 16 LPs (2026):
- Customer Obsession
- Ownership
- Invent and Simplify
- Are Right, A Lot
- Learn and Be Curious
- Hire and Develop the Best
- Insist on the Highest Standards
- Think Big
- Bias for Action
- Frugality
- Earn Trust
- Dive Deep
- Have Backbone; Disagree and Commit
- Deliver Results
- Strive to be Earth's Best Employer
- Success and Scale Bring Broad Responsibility
The most commonly probed LPs in SWE loops: Ownership, Bias for Action, Deliver Results, Dive Deep, Invent and Simplify, Have Backbone/Disagree and Commit.
What "good" looks like per LP:
Ownership: You did something that was not your job because it mattered. You took on a problem without being asked. The story should show you acting like an owner of the outcome, not an employee of a task.
Bias for Action: You made a decision with incomplete information. You moved fast, learned, and course-corrected. The failure mode: "we did a lot of analysis and then..." — Amazon doesn't want infinite analysis.
Dive Deep: You caught something others missed because you went to the actual data. You found the root cause, not just the symptom. Metrics, logs, specific numbers — depth is evidenced by specificity.
Have Backbone; Disagree and Commit: You pushed back on a manager or senior colleague with data. You made your case clearly and respectfully. Then — critically — when the decision went the other way, you committed fully. Failure mode: candidates who only have "I disagreed and won" stories.
Deliver Results: Quantified outcomes. Not "we improved latency" — "we reduced P99 latency from 800ms to 120ms, which cut error alerts by 73%." Amazon interviewers will probe for numbers; don't have to invent them, but you need to know them.
How to prepare STAR stories for Amazon
Write 8–10 stories. For each, capture:
- Situation: 2 sentences max
- Task: what was your specific responsibility
- Action: 70% of the answer — what YOU specifically did, not what the team did
- Result: quantified outcome + what you'd do differently
Map each story to 3–4 LPs (good stories flex). You don't need 16 separate stories — 8–10 strong stories, flexibly mapped, covers the loop.
Stories Amazon loves:
- You caught a critical bug or data issue no one else noticed (Dive Deep)
- You shipped something under deadline pressure despite obstacles (Deliver Results + Bias for Action)
- You simplified a complex system or process (Invent and Simplify)
- You disagreed with a technical direction, stated your case with data, were overruled, committed anyway (Have Backbone)
- You took ownership of a production incident outside your scope (Ownership)
The follow-up you must prepare for: interviewers will ask "what would you do differently?" for every story. Have a genuine answer — this is where candidates who memorized stories fall apart.
Part 2: Coding — Amazon's DSA bar
Amazon's coding bar is similar to Google's but with some distinct patterns.
Topic weighting (Amazon-specific):
- Arrays + strings: highest frequency
- Trees (BST, binary trees): very common
- Graphs: BFS/DFS, topological sort
- Dynamic programming: medium frequency (not as heavy as Google)
- Linked lists: moderate
- Design-in-coding: LRU cache, design a parking lot class, etc.
Amazon's coding interview quirks:
- Time pressure is real. Amazon coding rounds move faster than Google's. You have 45 minutes for 1–2 problems. If you over-explain your approach for 15 minutes, you may not finish.
- They want working code. Not pseudocode. Not "I'd implement it like this..." — actual code in your language of choice.
- Follow-ups are common. A medium problem often has a "now optimize it" or "what if the data is streaming?" follow-up. Save 10 minutes for this.
Frequently reported Amazon questions:
- Two Sum / 3 Sum — warm-up, must be instant
- Merge K Sorted Lists — heap, very common
- Word Break — DP, string problems common at Amazon
- Number of Islands — BFS/DFS on a grid
- Binary Tree Level Order Traversal — and variants
- Minimum Path Sum — DP on a grid
- Valid Parentheses / Generate Parentheses — stack
- Longest Palindromic Substring — DP or expand-from-center
- Find Median from Data Stream — two heaps
- Design an LRU Cache — HashMap + doubly linked list
What separates a hire from a no-hire on coding at Amazon:
- Clarity of communication (Amazon is explicit that they grade communication in every round)
- Edge case handling stated proactively
- Working code in reasonable time — not perfect code, but correct and complete
- Clean follow-up response: don't restart from scratch, build on what you have
Part 3: System design — what Amazon expects
Amazon's system design bar is high and specific — they want distributed systems thinking, not hand-wavy diagrams.
Common Amazon system design questions:
- Design Amazon's recommendation engine ("customers who bought X also bought Y")
- Design a rate limiter for the Amazon API Gateway
- Design S3 (or "design a blob storage system")
- Design Amazon SQS (distributed message queue)
- Design a URL shortener (bit.ly / Amazon short links)
- Design a real-time bidding system (Amazon ads)
- Design a notification system (Amazon SNS)
- Design an order management system (e2e from cart to fulfillment)
What Amazon system design interviewers look for specifically:
- Scalability instincts: "at 1M users this is fine; at 100M users, here's where it breaks and how I'd fix it"
- Data model clarity: table schema, partition key choices, consistency guarantees
- Queue-based thinking: Amazon loves asynchronous, queue-driven architectures (SQS, SNS, EventBridge); showing familiarity with this pattern plays well
- Failure modes: what happens when a service goes down? How do you ensure idempotency? How do you handle duplicate messages?
- Cost awareness: Amazon is famously frugal — a design that uses 10 services when 3 suffice will get probed
Frugality in system design: occasionally interviewers ask follow-up cost questions. Know roughly that: DynamoDB reads are cheap, full-text search (OpenSearch) is expensive, in-memory caches (ElastiCache) have significant costs at scale. You don't need exact pricing — just the directional sense.
The bar raiser round: how to handle it
The bar raiser is a wildcard. They may:
- Ask tougher LP questions (especially "tell me about a time you failed")
- Ask a harder coding question
- Go deeper on system design trade-offs
- Ask about a time you "raised the bar" — literally what the role is about
The bar raiser failure mode: candidates who flip to a different persona in the bar raiser round. Be consistent. The bar raiser has read your other interviewers' feedback — inconsistency is a red flag.
What the bar raiser is actually checking: whether you would make the surrounding team better. They're asking themselves "does hiring this person raise or dilute our team's average?" Technical competence is table stakes — they're grading for the evidence that you operate at a high level, independently.
Prep roadmap: 6 weeks to Amazon offer
Weeks 1–2: LP stories
- Write 8 stories, map to LPs, say each out loud until under 2 minutes
- Focus on quantified results and "what I would do differently"
Weeks 3–4: Coding
- 70 problems: heavy on arrays/strings, trees, graphs
- Amazon specific: practice writing complete working code fast — set 25-min timers
Week 5: System design
- Study 4 designs: distributed queue, blob storage, recommendation engine, rate limiter
- Practice drawing + explaining the data model first before the architecture
Week 6: Full mock loops
- 5 rounds in sequence, mixing LP + coding back-to-back
- Voice mock practice for LP: interviewers will cut you off if you ramble — practice staying under 2 minutes per answer
FAQ
How many Leadership Principles should I prepare stories for? Prepare 8–10 stories that flex across multiple LPs. Don't memorize 16 separate stories — a single strong story about taking ownership of a production incident can cover Ownership, Deliver Results, and Dive Deep simultaneously.
Does Amazon still do the "bar raiser" interview in 2026? Yes. The bar raiser model is a core part of Amazon's hiring process and has not changed. Every loop has a designated bar raiser who submits an independent hire/no-hire signal.
What coding language does Amazon prefer? Amazon accepts Python, Java, C++, JavaScript, and others. Use your strongest language — Amazon doesn't penalize for language choice. Python is most common among candidates; Java and C++ are also common.
How different is Amazon L4 vs L5 bar? L4 (SDE II equivalent): solid coding fundamentals, owns features end-to-end, LP stories at the individual contributor level. L5 (Senior SDE): system design is a must, LP stories show influence beyond your immediate team, data-backed decision making required.
Is the Amazon interview getting harder in 2026? The LP bar has risen as candidate preparation has improved — more candidates now have polished STAR stories. What differentiates in 2026: specificity (real numbers, real dates) and authenticity (genuine "what I'd do differently" answers). Interviewers can tell when a story was memorized vs. lived.