Amazon Machine Learning Engineer
Interview Guide

📊 Quick Stats

Timeline: 5-7 weeks | Difficulty: Hard | Total Comp (L5): $220-350K | Reapply: 12 months

What makes it unique: 16 Leadership Principles framework • Bar Raiser veto power • 60-70% behavioral focus • Back-loaded stock vesting (5/15/40/40)

The Gist

Amazon's analytics interview process is fundamentally different from other tech giants—it's driven by the 16 Leadership Principles, not just technical prowess. While Google and Meta allocate 60-70% of interview time to technical assessment, Amazon flips this: expect 60-70% behavioral depth and 30-40% technical evaluation. Your SQL skills matter, but your stories demonstrating Customer Obsession, Ownership, and Delivering Results matter more.

The Bar Raiser system is Amazon's unique hiring safeguard. A Bar Raiser is a specially trained senior employee from outside your target team who has veto power over hiring decisions. Even if four interviewers recommend "hire," the Bar Raiser can reject you if they believe you won't raise Amazon's talent bar. This ensures Amazon only hires people who elevate the organization, not just fill headcount.

Amazon's compensation structure is unusual: stock vests 5% / 15% / 40% / 40% over four years, creating a significant cliff. Years 1-2 see minimal equity vesting, offset by large sign-on bonuses (typically 50% in year 1, 50% in year 2). Years 3-4 bring substantial compensation increases as the bulk of stock vests. This structure is designed to retain talent through the full vesting period.

The culture emphasizes frugality—don't expect free gourmet meals or lavish perks like at Google or Meta. Door desks (built from doors to save money) remain symbols of doing more with less. Amazon invests in customers and business growth, not office amenities. Expect to work 50-60 hours during peak periods (Prime Day, holidays) and demonstrate ownership beyond your job description.

Preparation should focus on crafting 8-10 detailed STAR stories covering all 16 Leadership Principles with quantified impact. Practice behavioral questions extensively—interviewers will probe for 15-20 minutes on single stories. SQL proficiency is table stakes, but Leadership Principles alignment determines success.

What Does an Amazon Data Analyst Do?

As a data analyst at Amazon, you'll work across the world's most complex e-commerce and cloud infrastructure, analyzing customer behavior, optimizing supply chains, measuring advertising effectiveness, or supporting AWS product decisions. Your insights directly impact millions of customers and billions in revenue—whether improving Amazon.com's recommendation algorithm, optimizing Prime delivery routes, measuring Alexa's performance, or analyzing AWS usage patterns.

Day-to-day work involves writing SQL queries in Redshift and Athena, building dashboards in QuickSight or Tableau, designing and analyzing A/B experiments through Weblab (Amazon's proprietary platform), and partnering with product managers, engineers, and business leaders. When conversion rates drop, you investigate root causes. When new features launch, you measure impact. When leadership asks "should we expand into this new category?", you provide data-driven recommendations.

The technology ecosystem centers on AWS infrastructure: Redshift for data warehousing, Athena for querying S3 data lakes, QuickSight and Tableau for visualization, Python for analysis, and proprietary experimentation platforms. You'll learn Amazon-specific tools during onboarding—what matters in interviews is demonstrating SQL mastery, statistical rigor, and customer-obsessed analytical thinking.

Career progression follows Amazon's L-system: L4 for early-career analysts (0-3 years, $160-220K total comp), L5 for mid-level (2-5 years, $220-350K), L6 for senior (5-8 years, $350-550K), and L7 for principal roles (8+ years, $500-900K). Each level requires demonstrating next-level performance for 6-12 months before promotion. Note: Amazon levels are typically one lower than Google/Meta (Amazon L5 ≈ Google L4 ≈ Meta IC4), though total compensation is competitive.

Before You Apply

What Amazon Looks For

Amazon evaluates candidates on two equally critical dimensions: technical competence and Leadership Principles alignment. Neither alone is sufficient—you need both to succeed.

Technically, Amazon expects SQL mastery at scale—not just writing queries, but optimizing them for Redshift's columnar storage, understanding distribution keys, and reasoning through performance trade-offs with billions of rows. You'll need statistical fundamentals for designing valid A/B tests and interpreting results correctly. Business acumen is critical—can you translate ambiguous business questions into analytical frameworks and actionable recommendations?

Behaviorally, Amazon seeks people who demonstrate Customer Obsession (default to customer impact in every decision), Ownership (drive projects end-to-end without being asked), Bias for Action (move fast with 70% information), Dive Deep (attention to detail, skepticism of surface-level explanations), and Deliver Results (overcome obstacles to achieve quantified outcomes). These aren't abstract values—interviewers probe for specific examples with measurable impact.

Communication matters enormously. Amazon's 6-page narrative culture (no PowerPoint in senior meetings) demands clear, data-driven storytelling. Can you write concisely? Present complex analysis simply? Adapt communication for technical vs. business audiences?

Red flags that will hurt your candidacy: Lack of customer focus (only thinking about business metrics without customer experience). Vague stories without quantified impact. Blame-shifting or not owning failures. Perfectionism that slows action (conflicts with Bias for Action). Entitlement or focus on perks (signals misalignment with frugality culture). Poor fundamentals in SQL or statistics.

Prep Timeline

💡 Key Takeaway: Start preparing Leadership Principles stories 2-3 months early. Amazon's behavioral depth exceeds any other FAANG company—you need authentic, detailed examples with quantified impact.

3-4 months out:

• Study all 16 Leadership Principles deeply—read Amazon's definitions and reflect on your experiences
• Begin SQL practice focusing on business scenarios (LeetCode, HackerRank, DataLemur)
• Draft initial STAR stories (8-10 stories covering all principles)
• Research Amazon's business model—e-commerce, AWS, advertising, devices

1-2 months out:

• Refine STAR stories—add quantified impact (percentages, dollars, time saved) to every story
• Practice behavioral questions out loud—aim for 3-5 minute detailed answers
• Complete 30-50 SQL problems focusing on: window functions, JOINs, aggregations, performance
• Study A/B testing: hypothesis testing, sample size, guardrail metrics
• Schedule mock interviews—get feedback on story depth and authenticity

1-2 weeks out:

• Review all Leadership Principles and your mapped stories
• Practice explaining your thinking process out loud for SQL problems
• Prepare thoughtful questions for each interviewer (research their backgrounds on LinkedIn)
• Review AWS basics if interviewing for AWS roles (S3, Redshift, Athena concepts)

Interview Process

⏱️ Timeline Overview: 5-7 weeks total (faster than Google/Meta on average)

Format: 1 recruiter call → 1 OA/phone screen → 5-6 hour Loop interview → debrief → offer

Amazon's analytics interview has 4 main stages:

1. Recruiter Screen (30-45 min)

Quick conversation to assess basic fit and introduce Leadership Principles framework.

Questions:

• "Why Amazon?"
• "Walk me through your background and most significant accomplishments"
• "Tell me about a time you faced a difficult challenge" (testing for LP alignment)

Pass criteria: Clear communication, relevant experience, customer-centric thinking signals.

Timeline: 2-3 days for feedback, 1 week to OA/phone screen.

2. Online Assessment or Technical Phone Screen (60-90 min)

Option A: Online Assessment (OA)

• Timed SQL challenges on HackerRank (2-3 problems, 90 minutes)
• Work sample simulation analyzing business scenarios

Option B: Technical Phone Screen

• Live video call with SQL coding (CoderPad/HackerRank)
• 1-2 SQL problems testing business logic
• Metrics or business case discussion

Focus: SQL proficiency, analytical frameworks, customer-obsessed thinking

🎯 Success Checklist:

• ✓ Think out loud—explain your approach before coding
• ✓ Ask about customer impact—"How does this analysis help customers?"
• ✓ Handle edge cases (nulls, duplicates, data quality)
• ✓ Optimize for scale—discuss performance with large datasets
• ✓ Connect technical work to business outcomes

Timeline: 3-5 days for feedback. Strong performance advances to Loop.

3. The Loop - Full Interview Day (4-6 hours)

📋 What to Expect: 5-6 consecutive 45-60 minute interviews

Breaks: Usually 5-10 min between rounds

Format: Virtual (Amazon Chime) with shared coding environments

Critical Insight: Amazon's Loop is 60-70% behavioral. Your stories matter more than your SQL speed.

Round 1: SQL & Technical Analysis (60 min)

Leadership Principles: Dive Deep, Deliver Results Focus: SQL mastery and analytical thinking

• 2-3 SQL problems with complex business logic
• Performance optimization discussions
• Example: "Build seller performance dashboard showing sales, returns, inventory turnover with YoY comparisons"

Key evaluation: Technical depth, attention to detail, scalability thinking

Round 2: Business Intelligence / Metrics (60 min)

Leadership Principles: Customer Obsession, Think Big Focus: Metrics design and strategic judgment

• Product scenario requiring metrics definition
• A/B test design or dashboard scoping
• Example: "How would you evaluate launching a new Amazon category?"

Key evaluation: Customer-centric thinking, analytical frameworks, business acumen

💡 Pro Tip: Always frame metrics through customer lens first. "This improves customer experience by..." not just "This increases revenue by..."

Rounds 3-4: Behavioral Deep Dives (45-60 min each)

Leadership Principles: Varies (2-3 principles per round) Focus: Authenticity and cultural fit

Deep behavioral exploration using STAR format. Interviewers probe 15-20 minutes per story:

Common questions:

• Customer Obsession: "Tell me about going above and beyond for a customer"
• Ownership: "Describe a project you drove end-to-end without being asked"
• Bias for Action: "Tell me about deciding with incomplete data"
• Deliver Results: "Describe delivering despite significant obstacles"
• Dive Deep: "Walk me through your most complex analysis"

Amazon's approach: Interviewers challenge your stories. "Why that approach?" "What data specifically?" "Who else was involved?" "What would you do differently?"

Key evaluation: Authentic experiences, quantified impact, customer focus, ownership, learning from failures

Round 5: Case Study or System Design (60 min)

Leadership Principles: Invent and Simplify, Think Big Focus: End-to-end problem solving

• Business case: "Should Amazon offer free returns for non-Prime members? Analyze and recommend."
• OR Technical case: "Design analytics infrastructure for tracking delivery performance across 175 fulfillment centers"

Key evaluation: Structured thinking, trade-off analysis, customer impact prioritization

Round 6: Bar Raiser Interview (45-60 min)

Leadership Principles: All 16, heavy focus on Learning, Raising the Bar Who: Senior leader from outside your team with veto power

Bar Raiser Power: Can veto hire even if all others recommend "hire." Ensures talent bar rises.

Focus:

• Deep dive into 1-2 experiences demonstrating multiple Leadership Principles
• Learning agility: "Tell me about learning a completely new skill"
• Raising the bar: "What do you bring that makes Amazon better?"
• Long-term thinking: "Where do you want to be in 3-5 years?"

Key evaluation: Growth mindset, raising performance bar, cultural fit, potential not just current capability

4. Debrief and Decision (2-5 days)

All interviewers meet (or review feedback) to discuss your candidacy:

• ✅ Hire (move to offer)
• ❌ No hire (12-month cooldown)
• 🔄 Inclined but need more data (rare)

Bar Raiser facilitates and ensures hiring bar is maintained. Their opinion carries significant weight.

Key Topics to Study

SQL (Critical)

⚠️ Most Important: Amazon SQL questions test real business scenarios, not trivia. Practice translating business problems into queries.

Must-know concepts:

• JOINs (all types, especially self-joins for sequential analysis)
• Window functions (ROW_NUMBER, RANK, LAG/LEAD for cohort analysis)
• Aggregations with GROUP BY, HAVING
• CTEs and subqueries for readable complex logic
• Date manipulation for retention, cohort calculations
• CASE statements for conditional business logic
• Performance optimization for columnar databases (Redshift)

Redshift-specific:

• Distribution keys and sort keys (impacts query performance)
• Columnar storage implications
• VACUUM and ANALYZE operations

Practice platforms: LeetCode SQL, HackerRank, DataLemur, Skillvee

Leadership Principles (Critical)

16 Leadership Principles:

1. Customer Obsession
2. Ownership
3. Invent and Simplify
4. Are Right, A Lot
5. Learn and Be Curious
6. Hire and Develop the Best
7. Insist on the Highest Standards
8. Think Big
9. Bias for Action
10. Frugality
11. Earn Trust
12. Dive Deep
13. Have Backbone; Disagree and Commit
14. Deliver Results
15. Strive to be Earth's Best Employer
16. Success and Scale Bring Broad Responsibility

Preparation: Craft 8-10 STAR stories covering all principles with quantified impact

Product & Metrics (Important)

Amazon-specific metrics:

• E-commerce: conversion rate, cart abandonment, customer lifetime value, return rate
• AWS: service adoption, usage patterns, customer satisfaction (NPS)
• Advertising: click-through rate, return on ad spend, impression share
• Prime: membership retention, benefit utilization, subscription value

Frameworks:

• Metric definition (precise, actionable, customer-aligned)
• A/B test design (hypothesis, sample size, guardrails)
• Root cause analysis (data quality → segmentation → external factors)

Statistics & A/B Testing (Important)

Core concepts:

• Hypothesis testing, p-values, confidence intervals
• Type I/II errors, statistical power
• Sample size calculation
• Multiple testing corrections
• Causal inference basics

Amazon's Weblab platform: Proprietary A/B testing—you'll learn on the job

Compensation (2025)

💰 Total Compensation Breakdown

Amazon's back-loaded vesting (5/15/40/40) means years 1-2 have lower equity, offset by large sign-on bonuses.

Location Adjustments:

• 🌲 Seattle / Bay Area: 1.00x
• 🗽 NYC: 0.98x
• 🤠 Austin: 0.88-0.92x
• 🏠 Remote: 0.75-0.88x

🎯 Negotiation Strategy:

• Stock grants and sign-on bonuses are most flexible
• Focus on total comp over 4 years, not just year 1
• Competing Meta/Google offers provide strongest leverage
• Realistic increase: $30-70K with strong negotiation

Stock Vesting:

• Back-loaded: 5% / 15% / 40% / 40% over 4 years
• Vests semi-annually
• Sign-on offsets years 1-2 cliff (paid 50% year 1, 50% year 2)

Benefits:

• 10-20 days PTO (varies by level and tenure)
• 401(k) with 50% match up to 4% salary
• Comprehensive health/dental/vision
• 20 weeks parental leave (birth parent), 6 weeks (other parent)
• No free meals (frugality culture)

Your Action Plan

📚 Today:

1. Read all 16 Leadership Principles on Amazon's website
2. Assess SQL skills with 2-3 practice problems
3. List past experiences demonstrating Leadership Principles

📅 This Week:

1. Draft 8-10 STAR stories covering each Leadership Principle
2. Create 3-6 month study plan
3. Set up practice accounts (LeetCode SQL, HackerRank, Skillvee)

🎯 This Month:

1. Complete 30-50 SQL problems (window functions, JOINs, business scenarios)
2. Refine STAR stories—add quantified impact to each
3. Practice behavioral questions out loud (3-5 minute detailed answers)
4. Schedule 2-3 mock interviews

🚀 Ready to Practice?

Browse Amazon-specific interview questions and take AI-powered practice interviews on Skillvee to build confidence and get real-time feedback.

Remember: Customer Obsession + Ownership + Deliver Results = Success at Amazon! 🚀