Amazon - Behavioral Interview Guide¶

Amazon Interview Overview¶

Format: - 5-7 rounds (1 phone screen + 4-6 onsite/virtual + 1 "Bar Raiser") - Behavioral: 40-50% of total interview (HIGHEST among MAANG) - Technical: 50-60% - Duration: 60 min per round

Unique Features: - Bar Raiser: Independent interviewer ensures high hiring bar - Loop Interview: Each interviewer tests different Leadership Principles (LPs) - 16 Leadership Principles: Must prepare stories for ALL 16

Amazon's 16 Leadership Principles (LPs)¶

The full LP reference - each principle's definition, the questions it maps to, and what interviewers look for - lives in one canonical place so it stays consistent:

➡️ Leadership Principles guide (all 16, with definitions + question examples + a story hook each).

Below we focus on what's Amazon-interview-specific: how to TAG stories to LPs, a coverage matrix, and the do's/don'ts. Study the LP reference first, then map your own stories using the matrix.

How to tag a story to an LP (3 worked examples)¶

The technique Amazon rewards: name the LP, then tell a quantified STAR that demonstrates it. A few exemplars (you don't need one per LP - see the coverage matrix):

Ownership - "This shows Ownership."

S: Payment service (another team's) was down, ~$50K/hr revenue loss, owners asleep.
T: Fix it now rather than wait / escalate and stall.
A: Debugged it myself; found a third-party API timeout; shipped retry-with-backoff in 2h;
   wrote a post-mortem for the owning team + added a monitoring alert.
R: Stopped the bleed; owning team adopted the alert.  Learning: ownership = solve it, not assign blame.

Are Right, A Lot - "This shows Are Right, A Lot - including admitting when I was wrong."

S: Picking a DB; my instinct was MongoDB (familiar).
T: Make the right call, not the comfortable one.
A: Sought 3 senior perspectives; one flagged our data is relational; I researched, changed my mind, chose PostgreSQL.
R: Complex queries ~10x faster than the Mongo path would've been.  Learning: ego is the enemy of the right decision.

Have Backbone; Disagree and Commit - "This shows Have Backbone; Disagree and Commit."

S: Manager wanted a full rewrite; I favored incremental refactor.
T: Push back honestly, then align.
A: Presented data on rewrite-failure rates + an incremental alternative; manager still chose rewrite; I committed 100% and led it.
R: Rewrite shipped (a bit late) and enabled new features - I was partly wrong, and the trust grew.

For stories covering the other principles (Customer Obsession, Dive Deep, Frugality, etc.), reuse the hooks in the LP reference and flesh them out with your own numbers.

Amazon LP Coverage Matrix¶

Prepare 12-15 stories that each map to multiple LPs:

Story	Primary LP	Secondary LP
Optimized checkout	Customer Obsession	Deliver Results
Fixed production bug (not my team)	Ownership	Bias for Action
Automated deployments	Invent and Simplify	Frugality
Chose PostgreSQL over MongoDB	Are Right, A Lot	Learn and Be Curious
Learned Kubernetes	Learn and Be Curious	Deliver Results
Mentored junior engineer	Hire and Develop	Earn Trust
Pushed back on buggy release	Insist on Highest Standards	Have Backbone
Migrated to microservices	Think Big	Deliver Results
Added read replica under pressure	Bias for Action	Ownership
Used Prometheus vs paid tool	Frugality	Invent and Simplify
Missed deadline but told client early	Earn Trust	Deliver Results
Debugged latency issue	Dive Deep	Insist on Standards
Disagreed with manager	Have Backbone	Earn Trust

Amazon-Specific Do's and Don'ts¶

✅ Do:¶

Map every story to LPs - "This shows Ownership because..."
Use data - Amazon is metrics-driven
Show customer impact - Always bring it back to customer
Prepare 12-15 stories minimum (cover all 16 LPs)
Practice STAR format - Amazon invented it!
Show long-term thinking - Not just quick wins
Admit failures - Especially for "Deliver Results" (failure story)
Say "I" not "we" - Amazon wants YOUR contribution

❌ Don't:¶

Skip any LPs (Bar Raiser will notice gaps)
Only talk about successes (need failure stories too)
Take all credit ("I" good, but mention team support)
Focus only on technical (show leadership, people skills)
Give vague answers (Amazon wants specifics)

Questions to Ask Interviewer¶

Customer Obsession: - "Can you share an example of when your team went above and beyond for a customer?"

Leadership Principles: - "Which Leadership Principles are most important for this team?" - "How does [specific LP] manifest in your day-to-day work?"

Impact: - "What's the most impactful project this team has shipped recently?"

💡Pro Tips for Amazon¶

Learn the 16 LPs by heart - Interviewers will ask: "Which LP does this show?"
Use the LP language - Say "This demonstrates Customer Obsession because..."
12-15 stories minimum - Each story should map to 2-3 LPs
Prepare deep dives - Amazon interviewers will ask follow-ups for 10+ minutes on ONE story
Customer Obsession is #1 - Most important LP, always bring it back to customer
Failure stories required - Especially "Deliver Results" - tell me about a deadline you missed
"I" not "we" - Amazon wants to know YOUR role specifically
Quantify everything - $X revenue, Y% improvement, Z users impacted

Summary¶

Amazon wants: - 🎯 Customer-obsessed leaders - 💪 Owners who solve problems beyond job scope
- ⭐ High standards maintainers - 🚀 Big thinkers who deliver results - ⚡ Action-takers with calculated risks - 🤝 Trust earners through transparency

Prepare: - Memorize all 16 Leadership Principles - 12-15 stories covering all LPs (some stories hit multiple) - Practice STAR format religiously - Prepare deep dives (interviewers dig for 10 min per story) - Must have failure stories (Deliver Results, Are Right A Lot)

Remember: - Bar Raiser interview is hardest - different LP tested - Each interviewer assigned specific LPs before loop - 40-50% of interview is behavioral (highest of MAANG) - LP language is critical - speak Amazon's language

Next Steps: - Review full Leadership Principles guide - Create STAR stories for all 16 LPs - Practice saying which LP each story demonstrates