For a deeper understanding of Amazon's engineering culture and technical challenges, start with our Amazon engineering deep dive. Amazon's Leadership Principles-driven interview process is unique among FAANG — compare it to Google's or Meta's approaches to understand which culture fits you best.
For additional preparation, see our guide on Amazon Leadership Principles Interview: Guide with....
The behavioral round is where many candidates fall short — prepare with our behavioral interview guide.
Technical rounds at Amazon lean heavily on architecture — our system design interview guide covers the key patterns you'll need.
{
"@context": "https://schema.org",
"@type": "BlogPosting",
"headline": "Amazon Engineering Interview Guide",
"description": "Technical interview preparation for Amazon engineering roles: the Leadership Principles behavioral interview (the most distinctive aspect of Amazon.",
"datePublished": "2026-03-19",
"author": {
"@type": "Organization",
"name": "CodeSwiftr Team"
},
"url": "https://codeswiftr.com/blog/amazon-interview-guide"
}
Related Reading
- AI Interview Practice Tools Comparison
- Amazon Leadership Principles Interview Guide
- Accelerator Startup Interview Guide
- Affirm Software Engineer Interview Guide
- Airbnb Interview Guide
Elevate your prep with AI. Practice your technical interviews with CodeSwiftr and get real-time feedback on your delivery, STAR method compliance, and technical depth.
Explore Related Topics
- AMD Software Engineer Interview Guide
- Amazon Web Services (AWS) Engineering Interview Guide
- Amazon and AWS Engineering Interview Guide
Related Guides
- Amazon Leadership Principles Interview Questions
- System Design Interview Guide
- Behavioral Interview Star Method
Ready to practice? Start your free mock interview on CodeSwiftr.
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "How important are Amazon's Leadership Principles in the interview process?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Amazon's Leadership Principles (LPs) are central to every interview round, not just the behavioral round. Amazon interviewers are explicitly trained to assess LP alignment in every conversation, including coding rounds. Each LP question is scored independently and fed into a 'vote' system where interviewers are designated LP owners — they own specific principles and their vote carries extra weight on those principles. Most candidates fail Amazon interviews not due to technical weakness but due to weak LP responses: generic stories, no measurable outcomes, or failure to demonstrate ownership and bias for action."
}
},
{
"@type": "Question",
"name": "What is the Amazon bar raiser and what do they look for?",
"acceptedAnswer": {
"@type": "Answer",
"text": "The bar raiser is a trained, neutral interviewer from a different team whose sole job is to uphold Amazon's hiring bar. They have veto power over offers. Bar raisers are looking for candidates who are better than the bottom 50% of people currently in the role — Amazon calls this 'raising the bar.' They focus especially on Leadership Principles, probing for inconsistencies or inflated claims. In technical rounds, bar raisers often push candidates beyond their comfort zone to find where they break. The bar raiser is usually one of the last interviews in the loop."
}
},
{
"@type": "Question",
"name": "What coding topics does Amazon focus on in SDE interviews?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Amazon SDE interviews emphasize object-oriented design, arrays/strings, trees, and dynamic programming at LeetCode medium difficulty. OOP design problems are more common at Amazon than at Google or Meta — they test whether candidates can design extensible, maintainable systems. For SDE II and above, system design rounds are included and focus on distributed systems concepts: message queues (SQS), object storage (S3), caching strategies, and API gateway patterns — all of which are AWS services candidates are expected to know at a conceptual level."
}
},
{
"@type": "Question",
"name": "How should I structure STAR method answers for Amazon LP questions?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Amazon LP answers should follow the STAR framework (Situation, Task, Action, Result) with emphasis on Action and Result. Keep Situation/Task to 20% of your answer — interviewers care about what YOU specifically did, not context. For Action: use first-person singular ('I did X') not 'we.' For Result: include specific metrics (reduced latency by 40%, saved $2M annually, shipped to 10M users). Pre-prepare 6-8 strong stories that map to multiple LPs — 'Customer Obsession,' 'Ownership,' 'Invent and Simplify,' and 'Deliver Results' come up in nearly every Amazon loop."
}
}
]
}