Amazon runs a different interview loop than most big tech companies. The technical bar matters, but the behavioral bar is unusually high. Every round, including coding and design, checks for Leadership Principles.
If you are preparing for Amazon, this role-by-role breakdown from PracHub is a good starting point: Most Common Amazon Interview Questions by Role (2026).
What the Amazon interview process looks like
The structure is fairly consistent across roles:
Online Assessment (OA)
For SDE roles, this is usually 1-2 coding problems. For data roles, expect SQL and analytics-style questions. It is timed, often around 90 minutes.Phone screen
Usually one technical question and 1-2 behavioral questions tied to Leadership Principles.Onsite, usually a virtual loop
Expect 4-5 rounds, each around 45-60 minutes. Every round includes at least one behavioral question. One interviewer is the Bar Raiser, a trained interviewer from another team who can veto the hire.
That last point matters. Amazon does not treat behavioral as a warm-up. It is part of the decision in every round.
SDE interviews: coding first, behavior in every round
For Software Development Engineer roles, the process is coding-heavy, but behavioral prep is mandatory.
What shows up most often in coding rounds
PracHub has 160 Amazon coding questions in its dataset, and the common topics are pretty predictable:
- Arrays and strings
- Two pointers
- Sliding window
- Trees and graphs
- BFS and DFS
- Lowest common ancestor
- Dynamic programming, usually medium difficulty
- Data structure implementation, such as LRU cache
One thing that catches people off guard is the framing. Amazon often wraps standard problems in practical business scenarios like:
- warehouse optimization
- delivery routing
- inventory management
The underlying problem may still be a graph traversal or a sliding window question, but the prompt sounds like an operations problem.
System design for SDEs
PracHub lists 48 Amazon system design questions. The recurring themes are very Amazon-shaped:
- Design an order management system
- Design a product recommendation engine
- Design a delivery tracking system
- Design a pricing system with real-time updates
These are not abstract whiteboard exercises. You need to connect technical choices to scale, reliability, latency, and business impact.
Behavioral topics that come up again and again
PracHub tracks 122 Amazon behavioral questions, and some Leadership Principles show up far more often than others:
- Customer Obsession
- Ownership
- Dive Deep
- Bias for Action
- Deliver Results
Interviewers explicitly map your answers to these principles. They take notes on what you demonstrated, then compare impressions across the loop. If your examples are vague, you will feel that quickly.
Data Scientist interviews: SQL, experiments, and product metrics
Amazon Data Scientist interviews have a different balance. You still need strong behavioral answers, but the technical side leans toward analytics, experimentation, and applied ML.
PracHub's Amazon set includes 65 SQL questions and 71 ML questions. Common examples include:
- "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
What Amazon tends to care about in ML rounds
The ML areas called out in the source are tightly tied to Amazon's product and marketplace model:
- recommendation systems
- fraud detection
- demand forecasting
- NLP for review analysis
- search ranking
This is useful because it tells you where to focus. If your prep is centered on generic model trivia, you may miss what Amazon actually asks, applied questions tied to user behavior, marketplace integrity, or retail operations.
Product sense matters more than many candidates expect
Amazon DS interviews put real weight on product metrics. You need to explain how success is measured and how you would test changes. That means being comfortable with experiment design, tradeoffs in metrics, and the business meaning behind your analysis.
If you answer with technical detail but cannot define the right success metric, that is a problem.
Data Engineer interviews: heavy SQL and reliable pipelines
Data Engineer interviews at Amazon are very SQL-heavy. The source is direct about that, and it lines up with what candidates usually report.
Expect questions around:
- complex SQL on large datasets
- query optimization
- data modeling, such as star schema for e-commerce data
The design side focuses on data systems, not general backend design.
Common pipeline design themes
Typical prompts include:
- 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 cares about scale and reliability here. A clean architecture diagram is not enough. You need to explain what happens when jobs fail, when data arrives late, when retries create duplicates, or when upstream quality drops.
If you skip failure modes, your answer is incomplete.
What applies to every Amazon role
Some prep advice is role-specific. Some is universal.
1. Prepare 12-15 STAR stories
This is the biggest pattern in Amazon prep. You need a bank of stories mapped to Leadership Principles.
The source is blunt on this point. It is not optional.
A lot of candidates prepare hard for coding or SQL, then improvise behaviorals. That is a bad tradeoff for Amazon. Since every round includes behavioral questions, weak stories can sink an otherwise strong loop.
2. Be precise with metrics
Amazon is data-driven, and interviewers expect specifics. "We improved performance" is weak. "We cut latency by 28%" is useful.
The same applies to product work, incident response, project delivery, and system design. Use numbers whenever you can. If your example has no measurable result, it will sound unfinished.
3. Think in terms of the flywheel
This comes up most often in system design and product discussions. Amazon likes reasoning that connects technical choices to business outcomes through reinforcing loops.
If your design improves delivery speed, does that improve customer trust, which drives more usage and increases operational efficiency? That style of thinking tends to land well in Amazon interviews.
4. Understand what the Bar Raiser is doing
The Bar Raiser is not there to fill a seat for one team. This person is judging whether you meet Amazon's hiring standard overall.
That usually means close attention to Leadership Principles, quality of judgment, and consistency across rounds. If one round says you show strong Ownership and another suggests the opposite, that will come up in the final discussion.
How I would prep, based on this breakdown
If I were targeting Amazon, I would split prep like this:
- Build a Leadership Principles story bank first
- Practice role-specific technical questions second
- Rehearse answers with numbers, tradeoffs, and clear outcomes
- For design rounds, tie the system back to customer impact and business metrics
I would not prep from random lists alone. Amazon patterns are role-dependent. SDE, DS, and DE loops overlap on behaviorals, but the technical expectations are clearly different.
If you want to practice against a large role-specific set, PracHub has Amazon questions across coding, behavioral, ML, SQL, and system design here: interview questions on PracHub.
The useful part is the distribution: 160 coding, 122 behavioral, 71 ML, 65 SQL, and 48 system design questions from Amazon. That makes it easier to focus on what your target role is likely to test instead of studying everything equally.
For the full role-by-role breakdown, go back to the original PracHub post: Most Common Amazon Interview Questions by Role (2026).
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