Amazon Software Engineer Interview: Inside the Coding, Design, and Bar Raiser Rounds

Few interviews test both depth and discipline like the Amazon software engineer interview. Every round challenges you to think clearly, design systems that scale, and write code that works under pressure. It is a test of logic, communication, and ownership, which are qualities that define Amazon’s engineering culture. With Amazon Web Services serving over 1.4 million businesses worldwide, the scale you work with is unmatched.

As an engineer, you help design and optimize systems that reach hundreds of millions of users through AWS, Prime, and Alexa. This guide explains what the role demands, why it is one of the most rewarding paths in tech, and how to prepare for each stage of the interview process with confidence.

What does an Amazon software engineer do?

Amazon software engineers design, build, and maintain the infrastructure that powers global-scale applications. The role combines hands-on coding with architectural thinking and a deep sense of ownership for system reliability and performance.

Key responsibilities include:

• Writing efficient, readable, and tested code in languages such as Java, Python, or C++
• Designing APIs and distributed systems that handle millions of transactions per second
• Collaborating across teams to translate business requirements into scalable solutions
• Conducting code and design reviews to uphold technical standards
• Building observability, automation, and monitoring tools for system health

The engineering culture rewards curiosity, precision, and problem ownership. You are expected to dive deep into issues, simplify complex designs, and deliver measurable results that improve the customer experience.

Why this role at Amazon

Becoming a software engineer at Amazon means joining a company that pushes the boundaries of scale and innovation. You’ll work on systems that define modern commerce and cloud computing, solving real-world problems that affect millions of users every day.

The role offers technical autonomy, fast career growth, and a chance to shape products used worldwide. Engineers can move between teams like AWS, Alexa, or Prime Video, gaining exposure to different architectures and challenges. With competitive compensation, strong mentorship, and a culture built on long-term thinking, Amazon offers one of the most dynamic environments for engineers to grow and make an impact.

If you’re still comparing teams or roles, check out our full Amazon interview guide for a breakdown of how interviews work across SDE, data, and product positions.

The Amazon software engineer interview process is built to evaluate three things: your ability to solve problems efficiently, your understanding of scalable system design, and your alignment with Amazon’s Leadership Principles. It’s designed to reveal not only how you code, but how you think, communicate, and make decisions under pressure.

Online assessment

The process often begins with an online assessment for SDE I and SDE II candidates. You typically complete two to three timed algorithm questions covering arrays, hash maps, trees, graphs, and basic graph traversal patterns such as DFS. Some versions also include debugging tasks or code-quality checks that evaluate how you reason through edge cases and incomplete information.

This stage primarily screens for baseline data structures and algorithms proficiency, correctness under time pressure, and your ability to reason clearly through a solution rather than just produce working code.

Tip: Practice solving DSA problems end to end, including explaining your approach and validating edge cases. Interviewers expect clean logic and correct handling of boundary conditions, not just passing test cases.

Technical phone screen

Candidates who pass the assessment typically move to a technical phone screen lasting around 45 to 60 minutes. This round usually includes one data structures and algorithms problem, often involving DFS, trees, or recursion, along with a leadership principles question.

Interviewers focus heavily on how you explain your thinking. Even if you do not complete the full solution, clearly articulating the optimal approach, trade-offs, and edge cases is viewed positively.

Key expectations

• Walk through your logic step by step
• Identify edge cases and invalid inputs
• Explain time and space complexity
• Respond thoughtfully to follow-up questions

Tip: Be prepared for probing questions such as where your solution might fail, how it handles invalid input, or whether there is a more efficient approach.

Onsite interview loop

Candidates who advance move into a multi-round interview loop, typically consisting of three technical interviews and one dedicated leadership principles evaluation. The full loop may span several weeks, and the overall process can take up to two months from application to final feedback.

• Technical interviews: These rounds focus on data structures and algorithms, often involving graphs or trees, paired with leadership principles questions. Interviewers may push beyond the initial problem by asking about alternative approaches, better time or space complexity, and critical edge cases others might miss. Partial implementations are acceptable if the optimal solution is explained clearly.

• System design interview: One round may be fully dedicated to system design. Candidates have been asked to design a customer ratings and reviews system similar to an e-commerce product page. Interviewers expect you to draw the flow diagram, explain data flow, and reason through edge cases and failure scenarios. Follow-up questions are frequent and test depth of understanding rather than surface-level design.

• Leadership principles evaluation: This round focuses on behavioral questions such as handling difficult team challenges or decision-making under pressure. Interviewers cross-question responses extensively to test consistency, ownership, and clarity of thought.

Across all rounds, interviewers are known to probe deeply, often revisiting assumptions, edge cases, and design decisions to test conceptual understanding.

Tip: Practice defending your solutions. Be ready to explain why you chose a particular approach, where it might break, and how you would improve it given more time.

Bar raiser and hiring decision

Every interview loop includes a bar raiser, a trained interviewer responsible for maintaining Amazon’s hiring standard. The bar raiser often cross-questions candidates extensively, revisiting earlier answers and introducing new scenarios to test judgment, depth, and long-term potential.

After the loop, interviewers submit detailed written feedback for review. Decisions are based on technical depth, communication, leadership principles alignment, and consistency across rounds. Successful candidates move on to team matching and offer discussions.

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Amazon’s software engineer interview goes beyond technical skill. It evaluates how you think, reason, and solve problems at scale. You’ll be expected to write efficient algorithms, discuss trade-offs, and communicate your thought process clearly. Most interviews blend algorithmic questions, system design prompts, and behavioral discussions rooted in Amazon’s Leadership Principles.

Below are common coding and algorithm questions that appear in the Amazon software engineer interview. Before you dive into the examples below, browse the full set of Amazon interview questions to see the patterns Amazon uses across SDE, data, and backend roles.

Coding and algorithm interview questions

Coding is the foundation of the Amazon software engineer interview. These questions assess how you reason through problems, write efficient code, and optimize for performance. Interviewers look for clarity of thought, structured reasoning, and the ability to communicate trade-offs while coding.

1. How do you find the missing integer from an array of 1 to N?

   This problem tests your ability to reason about arithmetic and data structure properties. The optimal approach uses a summation or XOR method to find the missing number in O(n) time and O(1) space. Interviewers may follow up on overflow handling or input validation.

   Tip: Compare both brute-force and optimized approaches out loud before starting to code.

2. How would you search for a target value in a sorted 2D matrix?

   This question checks your ability to apply binary search logic across two dimensions. You’ll need to reason about matrix indexing and optimize traversal to achieve O(log n) complexity.

   Tip: Clarify the matrix’s sorting pattern first, then outline how your search narrows down each iteration.

3. How do you rotate an array by k positions?

   This common array manipulation question tests in-place algorithm design. The most efficient method uses reversals or modular arithmetic to rotate without extra space.

   Tip: Walk through a small example manually to confirm your index logic before implementing it.

4. How can you find the total number of unique conversation threads?

   This question tests your ability to group related data efficiently using graph or union-find concepts. You’ll need to count connected components while managing memory and time complexity.

   Tip: Discuss how your approach handles large datasets or streaming data to show scalability awareness.

5. How would you justify an array of words given an array of widths?

   This text alignment problem evaluates your ability to manage logic and constraints. You’ll implement a greedy approach to distribute spaces while ensuring each line meets the target width.

   Tip: Address edge cases like the last line, short words, and uneven spacing before coding.

6. How would you implement a basic LRU cache?

   This design problem tests how well you combine data structures for optimal performance. The ideal solution uses a hash map for O(1) lookups and a doubly linked list for O(1) eviction.

   Tip: Sketch the data structure and explain get and put operations clearly before you code.

7. Find the number of distinct islands in a matrix using DFS

   This question evaluates your ability to traverse a grid using depth-first search while handling visited states correctly. Interviewers focus on clean traversal logic, correct boundary checks, and how you differentiate between distinct island shapes or connected components.

8. How do you implement a priority queue using a linked list?

   This tests your understanding of ordered data insertion and retrieval. You’ll maintain a sorted structure that supports efficient peek and delete operations.

   Tip: State the time complexity of insert, delete, and peek before implementing your solution.

   You can practice this exact problem on the Interview Query dashboard, shown below. The platform lets you write and test SQL queries, view accepted solutions, and compare your performance with thousands of other learners. Features like AI coaching, submission stats, and language breakdowns help you identify areas to improve and prepare more effectively for data interviews at scale.

If you’re also shoring up your SQL fundamentals alongside algorithms, this guide on how long it takes to learn SQL gives realistic timelines and practice strategies.

System design interview questions

System design questions assess how you approach complex technical problems, translate business requirements into scalable architectures, and reason through trade-offs in performance, reliability, and maintainability. Amazon values engineers who can design practical, modular systems that align with its principles of ownership, simplicity, and long-term scalability.

1. How would you design a data mart or data warehouse for a new online retailer?

   Start by structuring raw transactional data into a dimensional model. Use fact tables like Sales, Orders, and Returns, and dimension tables for Customer, Product, Store, and Time to enable flexible analytics. Discuss ETL/ELT pipelines using AWS Glue or Redshift, and explain how you ensure data quality and incremental updates.

   Tip: Begin with a clear schema diagram, explain your data grain, and justify your partitioning and indexing strategy.

2. How would you design a database for a stand-alone fast food restaurant, and then answer KPI queries from it?

   Normalize transactional tables for Orders, MenuItems, and Customers while maintaining easy joins for reporting. Use proper indexing on frequently queried columns like OrderDate and MenuItemID. You can later denormalize into a small data mart to track metrics like top-selling items and customer ordering patterns.

   Tip: Mention how you’d balance OLTP efficiency with OLAP needs, such as using materialized views for real-time insights.

3. How would you design a relational music database for a service like Spotify?

   Define entities for Artist, Album, Track, and Genre, using bridge tables for many-to-many relationships between tracks and artists. Discuss schema evolution for adding metadata such as language, region, or licensing information.

   Tip: Explain how you’d design indexes for fast search and retrieval while maintaining referential integrity across millions of tracks.

4. How would you design a scalable product review system for Amazon.com?

   This question tests your ability to manage high-volume user-generated content. You’ll need to support real-time review submission, aggregation, and spam detection. Describe a design using partitioned NoSQL storage (like DynamoDB), caching layers for popular items, and asynchronous moderation pipelines.

   Tip: Discuss consistency trade-offs between immediate user visibility and delayed aggregation for analytics.

5. How would you design a recommendation engine for Amazon’s homepage?

   You’ll need to combine user activity logs, product metadata, and historical purchase data to generate personalized recommendations. Explain how you would build batch and streaming pipelines using tools like Kinesis or SageMaker, and how you’d rank results for relevance.

   Tip: Address data freshness and explain how you’d A/B test your recommendation models to validate accuracy.

6. How would you design an inventory management system for Amazon’s fulfillment centers?

   This problem focuses on reliability, synchronization, and visibility across distributed warehouses. You’ll need to track item quantities, handle concurrent updates, and ensure consistency between online orders and physical stock.

   Tip: Explain how you’d use event-driven architecture and idempotent updates to prevent double-counting or stock mismatches.

Behavioral and leadership principle interview questions

Behavioral interviews at Amazon assess how you think, act, and make decisions through the lens of the company’s 16 Leadership Principles. You’ll need to show ownership, curiosity, and a results-driven mindset through real examples. Interviewers want structured, data-backed stories that demonstrate how you’ve faced challenges, delivered value, and learned from experience.

1. Why do you want to work at Amazon?

   This question checks your motivation and understanding of Amazon’s mission. Your answer should connect your goals with Amazon’s culture of innovation and customer obsession.

   Tip: Show alignment between your career ambitions and Amazon’s scale and impact. Reference how you can grow while contributing to real customer outcomes.

   Sample answer: I’m drawn to Amazon because it combines large-scale technical challenges with a culture that values ownership. I want to work on systems that directly impact how millions shop, and I admire how Amazon empowers engineers to experiment, fail fast, and learn continuously.

2. Tell me about a time when you exceeded expectations during a project.

   Amazon looks for engineers who go beyond their assigned tasks to deliver measurable impact. The focus should be on initiative, creativity, and outcomes.

   Tip: Use metrics to highlight your contribution. Focus on how your initiative led to tangible improvements in performance, efficiency, or experience.

   Sample answer: During a service migration, I noticed latency spikes from inefficient API calls. I redesigned the caching strategy, reducing p99 latency by 45 percent and cutting infrastructure costs by 20 percent. This exceeded the original project goals and was later adopted by two other teams.

3. Tell me about a time you had to influence a technical decision without authority.

   This question measures your ability to earn trust and persuade through logic, data, and collaboration.

   Tip: Explain how you built consensus and used data or prototypes to convince others. Highlight emotional intelligence as much as technical reasoning.

   Sample answer: Our team debated whether to migrate a critical service to Kubernetes. I benchmarked performance, ran a cost simulation, and presented clear metrics showing a 30 percent cost reduction. My analysis shifted the team’s decision, and I led the rollout plan that saved over $250,000 annually.

4. Tell me about a time you received tough feedback. What did you do afterward?

   Amazon values humility and a growth mindset. They want to see that you take feedback constructively and turn it into action.

   Tip: Focus on your response to the feedback and how you implemented long-term improvements. Avoid being defensive or vague.

   Sample answer: My manager once pointed out that my design documents lacked clarity for non-technical stakeholders. I started reviewing drafts with a product manager before final submissions, incorporated visual architecture diagrams, and requested follow-up feedback. Within a quarter, my documents became team-wide templates.

5. Describe a data project you worked on. What were some of the challenges you faced?

   This question evaluates your problem-solving and ownership skills, especially when faced with ambiguous data or unclear requirements.

   Tip: Emphasize your approach to debugging, collaboration, and ensuring reliability under pressure.

   Sample answer: I once inherited a failing ETL pipeline that caused daily report delays. I rewrote sections of the job to use parallelized Spark operations, added data validation checks, and improved monitoring alerts. As a result, pipeline reliability improved from 70 percent to 99.8 percent uptime within two sprints.

Want more hands-on prep? Test your skills with real-world analytics challenges and tackle actual interview questions from top tech and data companies to sharpen your problem-solving and master interview-style questions end to end.

Succeeding in the Amazon software engineer interview requires more than coding ability. You’ll need to combine technical precision, customer-focused thinking, and strong alignment with Amazon’s Leadership Principles. Here’s how to prepare comprehensively.

For a more structured roadmap, explore Interview Query’s learning paths, which bundle algorithms, system design, and behavioral prep into guided tracks.

• Master core algorithms and data structures

Focus on problems involving arrays, trees, graphs, recursion, and dynamic programming, with particular emphasis on DFS and tree-based questions. Practice explaining your reasoning clearly as you work, even if you do not complete the full implementation. Interviewers value candidates who can articulate the optimal approach, identify edge cases, and discuss time and space tradeoffs.

Tip: Prioritize correctness first, then optimization. If you run out of time, clearly explain how you would improve the solution and why.

• Strengthen your system design fundamentals

System design plays a key role for mid- and senior-level (L5+) candidates. Practice common design patterns and be prepared to walk through full flows, including data models, request handling, and failure scenarios. Recent candidates noted that interviewers probe deeply into assumptions, invalid inputs, and where a design might break.

Tip: Justify every design choice. Be explicit about scalability, latency, and cost tradeoffs, and explain how you would evolve the system over time.

• Understand Amazon’s architecture mindset

Amazon favors modular, service-oriented design and expects candidates to think critically about edge cases and system boundaries. Be ready to explain how your services communicate, fail gracefully, and scale under load.

Tip: Practice drawing simple flow diagrams and walking through them step by step, including what happens when inputs are unexpected or malformed.

• Prepare your Leadership Principle stories

Every interview includes Leadership Principles questions, often paired with technical rounds. Prepare concrete examples that demonstrate how you handled difficult challenges, collaborated under pressure, or made tradeoffs with incomplete information. Interviewers frequently cross-question stories to test depth and consistency.

Tip: Use clear STAR narratives and focus on what you learned, not just the outcome.

• Develop familiarity with Amazon’s development practices

Understand how Amazon teams think about ownership, long-term maintainability, and operational responsibility. Be ready to discuss how you would test, monitor, and support systems after launch.

Tip: Highlight experiences where you maintained or improved systems over time, especially when dealing with failures or unexpected behavior.

• Practice clear and structured communication

Clear communication is critical throughout the interview loop. Narrate your thought process, confirm assumptions early, and respond calmly to rapid follow-up questions. Interviewers assess how well you defend your decisions as much as the decisions themselves.

Tip: If you get stuck, explain your debugging strategy and where you think the issue lies. Transparency in reasoning is viewed positively.

• Simulate the full interview experience

Use full mock interviews to replicate Amazon’s interview style, including coding rounds with follow-up probing, system design discussions, and Leadership Principles evaluation. Reviewing other candidates’ interview experiences and detailed problem discussions can also help you anticipate the depth of questioning. Interview Query’s AI interview tool can further help you practice responding to iterative follow-ups.

Tip: Treat preparation as iterative. Candidates emphasized persistence, including reapplying and seeking referrals, as a normal and expected part of the process.

Amazon software engineers in the United States earn highly competitive salaries that reflect their impact on large-scale, customer-facing systems. According to Levels.fyi, total annual compensation typically ranges from $180K for entry-level (SDE I) to over $1.66M for distinguished engineers (L10), with a median total compensation around $264K per year. Packages include base salary, stock grants, and performance-based bonuses, with significant growth as engineers move through senior and principal levels.

Regional salary comparisons

Compensation varies significantly by region, depending on living costs and equity value.

For other regions, compensation data can be viewed on Levels.fyi.

Amazon’s compensation structure rewards ownership and long-term performance. Stock awards typically vest on a 5%, 15%, 40%, 40% schedule, emphasizing sustained contribution and retention. Engineers who consistently raise the bar often receive annual stock refreshers, aligning their rewards with the company’s continued growth.

How long does the Amazon software engineer interview process take?

The process usually takes three to six weeks from the initial recruiter call to the final offer. Candidates often complete an online assessment, one or two technical phone interviews, and four to five on-site rounds covering coding, system design, and behavioral interviews.

What is the Amazon software engineer interview process like?

Expect multiple rounds focused on coding, system design, and behavioral questions aligned with Amazon’s Leadership Principles. Early rounds focus on algorithms and data structures, while later interviews emphasize scalability, architecture, and ownership.

What kind of coding questions does Amazon ask?

Amazon software engineer interview questions often center around arrays, strings, trees, graphs, dynamic programming, and recursion. Problems typically mirror medium-to-hard LeetCode questions. Candidates are also expected to discuss trade-offs and optimize for time and space complexity.

How can I prepare for the Amazon software engineer interview?

Focus on three areas:

• Master algorithms and data structures through structured daily practice.
• Strengthen system design fundamentals, especially using AWS tools like DynamoDB, S3, and Lambda.
• Prepare STAR-format stories that demonstrate Amazon’s Leadership Principles, such as Ownership and Deliver Results.

What are the levels for software engineers at Amazon?

Amazon’s software engineering roles are structured as follows:

• L4 – SDE I: Entry-level engineer focused on feature development and bug fixing.
• L5 – SDE II: Mid-level engineer handling system components independently.
• L6 – SDE III: Senior engineer leading projects and mentoring others.
• L7 – Principal SDE: Technical leader driving architecture across teams.
• L8+ – Senior Principal and Distinguished Engineer: Company-wide technical vision and leadership.

How important are Amazon’s Leadership Principles during interviews?

They are essential. Every interviewer evaluates candidates based on at least two of Amazon’s 16 Leadership Principles. You’ll need to demonstrate behaviors like Dive Deep, Invent and Simplify, and Bias for Action through real, measurable examples from your experience.

Does Amazon test system design for all levels?

No. System design interviews are generally required from L5 (SDE II) and above. Entry-level candidates focus on algorithms and problem-solving, while senior engineers must design scalable, distributed systems and explain trade-offs.

What tools or languages should I know for Amazon interviews?

Amazon primarily uses Java, Python, C++, and Go. You should be fluent in at least one of these languages and comfortable using it for algorithmic and design challenges. AWS familiarity is also advantageous, especially for backend or infrastructure-focused roles.

What are the compensation levels for Amazon software engineers?

According to Levels.fyi, total annual compensation ranges from $180K for SDE I to $1.66M for Distinguished Engineers. Packages typically include base salary, stock options that vest over four years, and annual performance bonuses.

What matters more: finishing the code or explaining the solution clearly?

Interviewers place significant weight on conceptual understanding and communication. Candidates who explained the optimal approach, identified edge cases, and discussed improvements were still evaluated positively even when the full solution was not completed within the time limit.

Landing a software engineering role at Amazon takes structured preparation, technical depth, and strong alignment with the company’s Leadership Principles. Each round of the interview tests how you solve complex problems, communicate trade-offs, and think like an engineer who builds at scale. To stand out, focus on mastering algorithms, system design, and behavioral storytelling while keeping your reasoning clear and concise.

Begin your preparation by practicing Amazon software engineer interview questions on Interview Query and simulating full interview loops. Combine this with mock interviews and learning paths that mirror real Amazon challenges. The key to success is consistency and clarity—approach every question like you would at Amazon: with ownership, curiosity, and customer focus.