Most Common Amazon Interview Questions by Role (2026)

Amazon interviews more candidates than almost any other tech company, and their process is distinctive. Every round — including technical ones — tests Leadership Principles. The behavioral component is heavier than at any other FAANG company.

The Amazon interview structure

Regardless of role, the structure is similar:

Online Assessment (OA) — 1-2 coding problems (SDE) or SQL + analytics problems (DS/DE). Timed, usually 90 minutes.
Phone screen — One technical question plus 1-2 behavioral questions tied to Leadership Principles.
Onsite (virtual loop) — 4-5 rounds, each 45-60 minutes. Every round has at least one behavioral question. One of the interviewers is the "Bar Raiser" — a trained interviewer from a different team who has veto power.

Software Development Engineer (SDE)

Amazon SDE interviews are coding-heavy with mandatory behavioral.

Coding questions (160 on PracHub from Amazon): Most common topics:

Arrays and strings (two pointers, sliding window)
Trees and graphs (BFS, DFS, lowest common ancestor)
Dynamic programming (medium difficulty)
Design questions (LRU cache, implement data structures)

Amazon coding questions tend to be practical. They will often frame a problem around an Amazon scenario: warehouse optimization, delivery routing, inventory management.

System design (48 questions):

Design an order management system
Design a product recommendation engine
Design Amazon's delivery tracking system
Design a pricing system with real-time updates

Behavioral (122 questions): Every round includes 1-2 behavioral questions. The most tested Leadership Principles:

Customer Obsession (tested in almost every loop)
Ownership
Dive Deep
Bias for Action
Deliver Results

Amazon interviewers explicitly map your answers to Leadership Principles. They take notes on which principles you demonstrated and compare across the loop.

Data Scientist

SQL and analytics (65 SQL + 71 ML questions):

"Write a query to calculate customer lifetime value"
"Design an experiment to test a new recommendation algorithm"
"How would you detect fraudulent seller accounts?"
Retention analysis, funnel analysis, cohort analysis

ML focus areas at Amazon:

Recommendation systems (product recommendations, "customers also bought")
Fraud detection
Demand forecasting
NLP for review analysis
Search ranking

Product sense: Amazon DS roles test product metrics heavily. You need to understand how Amazon measures success for its products and how to design experiments.

Data Engineer

SQL (heavy):

Complex queries on large datasets
Query optimization
Data modeling (star schema for e-commerce data)

Pipeline design:

Design an ETL pipeline for order data
Handle late-arriving data
Design a data quality monitoring system
Migrate from batch to real-time processing

Amazon DE interviews care about scale and reliability. They want to know how your pipeline handles failures, retries, and data quality issues.

Tips that apply across all Amazon roles

Prepare 12-15 STAR stories mapped to Leadership Principles. This is not optional. Amazon's behavioral bar is high, and fumbling these questions is the most common reason candidates fail.

Use the "flywheel" in system design. Amazon thinks in terms of virtuous cycles. If you can connect your design to business outcomes, it resonates.

Be specific about data and metrics. Amazon is a data-driven company. Vague answers like "we improved performance" are not enough. They want numbers.

The Bar Raiser is watching for culture fit. This person evaluates whether you meet Amazon's hiring bar, independent of the team's needs. They focus heavily on Leadership Principles.

PracHub has 466 questions from Amazon across all categories: 160 coding, 122 behavioral, 71 ML, 65 SQL, 48 system design. Filter by Amazon + your target role to see exactly what to expect.