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Amazon interview notes, page 90 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·Data Scientist·Technical Phone Screen

Jun 2026
Amazon Data Scientist SQL round, three questions back to back all on the same e-commerce schema. Felt like a reasonable set until the window function question made me second-guess my own syntax.
  • Given an orders table with a product category and sales amount, return the top 3 product categories by total sales for each calendar month. Ties should be broken by total sales descending, then category name alphabetically.
  • For every customer, produce a result showing order ID, order date, amount, and a running cumulative total of that customer's spending ordered by order date.
  • Classify each order as High (amount over 200), Medium (between 100 and 200 inclusive), or Low (everything else), then return the count of orders in each classification.

“I went straight for RANK() partitioned by month and ordered by sum descending, which was the right instinct.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon SWE system design round, one question the whole time. Pretty standard distributed systems territory but there's more to it than you'd think once you get into the weeds.
  • Design a job scheduling system for a distributed environment.

“I started with a single-node scheduler and worked outward from there, which in hindsight was probably the right move but I spent too long on it before the interviewer nudged me toward fault tolerance.”

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Amazon·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Amazon system design round for a software engineer role. One big open-ended question about building a streaming log pipeline, and they really wanted to see how deep you'd go on the operational side, not just the happy path.
  • Design a core component for a streaming system that ingests log events from multiple upstream services. The system must filter for error logs only, track per-service error counts in real time, compute a moving average of error rates over a sliding time window, and trigger alerts when a service crosses a defined threshold. Walk through your architecture, state management, windowing strategy, and how you handle late-arriving events, horizontal scale, and fault tolerance.

“I started with the filter layer and Kafka partitioned by service_id, which felt solid.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Amazon software engineer coding round with two back-to-back problems. Nothing too wild but the LRU cache had more moving parts than I expected and I spent way too long second-guessing my doubly linked list logic.
  • Design and implement an LRU cache with O(1) get and put operations, including eviction of the least recently used item when capacity is exceeded.
  • Rotate an n x n matrix 90 degrees clockwise in place without allocating a second matrix.

“I knew the answer involved a hashmap plus a doubly linked list but explaining why out loud while also coding it was harder than I anticipated.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Amazon ML Engineer screen, just one coding problem but it felt like they were watching how I thought through it more than whether I got the answer.
  • Given an array of integers and a target value, find the indices of two distinct elements that sum to the target.

“Classic two-sum.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon SWE interview with a stack-based algorithm problem. The question was straightforward to understand but the O(N^2) constraint meant you had to actually think before coding.
  • Given an array of integers representing task priorities that are processed right to left, return an array where each element is the index distance to the first smaller value to its right (or 0 if none exists). You must do better than O(N^2).

“Knew immediately this was a monotonic stack problem but explaining WHY took me longer than it should have.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Amazon for a solutions architect role, pretty much one question that took up most of the time.
  • Can you describe an experience that significantly changed your way of thinking or approach to a situation?

“Classic behavioral but it still tripped me up a bit.”

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Amazon·Product Manager·Onsite - System Design / Architecture

Jun 2026
Amazon PM interview with a system design question focused on async communication. Pretty technical for a PM role, felt more like an engineering screen at points.
  • Design a web service that supports asynchronous communication between components.

“I went straight to message queues and talked through producers and consumers, but I think I spent too long on the happy path and barely touched failure modes.”

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Amazon·Technical Program Manager·Onsite - Behavioral / Leadership

Jun 2026
Amazon TPM behavioral round, just one question but it hit harder than expected. The kind of question that sounds easy until you're sitting there trying to pick the right story.
  • Tell me about a time you made an error in judgment within the past year or two. What did you do about it and what was the result?

“I had a story ready but second-guessed it halfway through because the mistake felt too small.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2026
Amazon Applied Scientist screen with a single meaty A/B testing scenario that branched into a pretty deep stats conversation. The question felt deceptively straightforward at first but the follow-up about sparse strata is where it got real.
  • Amazon rolls out a new product-detail page UI. The A/B test shows click-through rate went up but conversion rate stayed flat. Walk through the statistical reasons why that might happen.
  • If you stratify the user sample by segment and one stratum ends up with very little data, what statistical approach lets you still draw useful conclusions from it?

“I started with novelty effect and user misguidance, which felt obvious, but I think the interviewer wanted me to go deeper faster.”

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