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Amazon interview notes, page 113 of 159

FAANG / Big Tech

Most of these Amazon write-ups come from Software Engineers (1,092), then Data Scientists (182) and Product Managers (165). The most reported rounds are the technical phone screen (618), the behavioral / leadership round (309), the online assessment (228) and the system design round (159).

1581 interview notes · updated Oct 2026

Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE coding round with a graph traversal problem centered on service dependency propagation. Pretty standard BFS/DFS territory but the problem statement was dressed up in Amazon infrastructure framing which threw me off for a second.
  • Given a directed dependency graph of services and a list of services that have been shut down, return all services that are affected (directly or transitively) by those shutdowns.

“The problem wraps a pretty classic BFS in an Amazon-flavored story about microservices going down.”

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Amazon·Product Manager·Onsite - System Design / Architecture

Jun 2026
Amazon PM interview focused entirely on system design and product thinking around Alexa's knowledge capabilities. Three connected questions, each building on the last, which I didn't fully anticipate going in.
  • Design a full data pipeline that lets Alexa answer questions about holidays around the world. Walk through data sources, how you'd ingest and normalize the data, where you'd store it, and how the query layer would work. Also, how do you reconcile different calendar systems like Gregorian, Lunar, and federal calendars, and keep everything up to date?
  • Now extend that same pipeline to handle animal-related questions. What new data elements or taxonomies do you need? And how do you deal with misclassified queries, like someone asking about Peppa Pig, which is a cartoon character but might get routed to the animal knowledge graph?
  • You're looking at error logs showing Alexa is failing on certain animal queries. How do you build a framework to categorize those errors, trace back to root causes, and decide what to fix first?

“I spent too long on the storage layer and not enough on the calendar reconciliation piece, which is clearly the interesting part.”

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Amazon·Data Scientist·Technical Phone Screen

Jun 2026
Amazon data scientist interview with a product analytics case around an energy-usage dashboard redesign. Pretty standard experimentation setup but the depth they wanted on statistical methodology caught me a bit off guard.
  • The product team is launching a redesigned energy-usage dashboard. How would you design an A/B test to measure whether it improves user engagement, and walk through your choice of metrics, statistical approach, and how you'd determine test duration?

“This is the kind of question where you can easily go wide and shallow and they'll just keep poking until you fall apart.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon data engineer interview with a SQL-heavy question about ranking salaries within departments. Nothing too wild but it required more thought than I expected on the edge cases.
  • Write a SQL query to find the top 3 distinct salaries within each department and return all employees who earn those salaries.

“I went straight for DENSE_RANK and partitioned by department, ordered by salary descending, then filtered where rank <= 3.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon QA engineer interview with a coding-style question. Pretty short session, just one problem thrown at me with not much else to go on.
  • Given a string, find the first character that appears more than once.

“Classic problem but I fumbled the first pass a bit.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE coding round with a string reorganization problem. Pretty standard algorithmic question but the edge case tripped me up more than I expected.
  • Given a string of lowercase letters, rearrange it so that no two adjacent characters are the same. If no valid arrangement exists, return an empty string.

“The 'aba' case was obvious but I fumbled explaining why 'aaab' is impossible.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon software engineer interview with a challenge project question. Not a lot of detail to go on but the focus was on how you approached a hard problem and whether you looped in others before committing to a solution.
  • Tell me about a challenging project you worked on. Did you consider other approaches before implementing your solution, and did you involve others in that decision?

“The follow-up about whether I explored alternatives before jumping in is the part that trips people up.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE coding round focused on a linked list deep-copy problem. Pretty standard LC 138 territory but they pushed hard on the space complexity tradeoff and a follow-up about mutating the original list.
  • Deep clone a linked list where each node has both a next pointer and a random pointer that can point anywhere in the list or be null. Do it in O(n) time.
  • Can you solve the same deep-clone problem without any extra space beyond the output itself?
  • What if you are not allowed to mutate the original list at all? How does that change your approach?

“Started with the two-pass hashmap because it's the easiest to explain out loud.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Amazon for an Applied Scientist role, just the one question but it took up most of the time. Pretty standard leadership principles territory but the follow-ups pushed harder than I expected.
  • Tell me about a time you were working under a tight deadline. How did you prioritize, what trade-offs did you make around scope or quality, and did you pull in help from others? What ended up happening?

“The trade-offs part is where I fumbled a bit.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Amazon SWE behavioral round, basically just two questions but they were meaty enough to fill the whole session. The format was pretty structured around leadership principles so come ready to go deep on specifics.
  • Tell me about a time you exceeded expectations. Walk through the context, what you did, who was involved, and what the measurable outcome was. Include any feedback you got and what you'd do differently.
  • Describe a time you fell below expectations. Same level of detail: context, actions, stakeholders, results, root cause, lessons, and what you did afterward to make sure it didn't happen again.

“The tricky part isn't having a story, it's having numbers.”

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