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Amazon interview notes, page 154 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·Onsite - Behavioral / Leadership

Jul 2026
Amazon SWE behavioral loop, four questions that all had some version of 'show me receipts' built in. Nothing too surprising but the depth expected on each one was more than I anticipated.
  • Give three words that describe you as a professional, and back each one up with a concrete example.
  • Walk me through how you set and track your personal or professional goals.
  • Describe a research paper you read recently: what problem it tackled, the core idea, the results, and what you personally took away from it.
  • Think of a competition where you didn't win. Why was the top solution better than yours, and what did you actually change afterward?

“I had my three words ready but the examples felt thin under follow-up.”

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Amazon·Data Scientist·Onsite - System Design / Architecture

Jul 2026
Amazon Data Scientist loop with a combined system design and coding round. Two pretty different problems back to back, which made the whole thing feel a bit disjointed, but I got through it.
  • Design a distributed order-processing system that is both highly available and low-latency. Walk through the architecture, how you'd scale it, and what fault-tolerance mechanisms you'd put in place.
  • Implement LeetCode 987, vertical order traversal of a binary tree, and explain the time and space complexity of your solution.

“I started with a pretty standard event-driven setup, message queue in the middle, separate services for inventory, payment, and fulfillment.”

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Amazon·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Amazon ML Engineer coding round, got a classic interval merging problem dressed up in a conference room scheduling scenario. Pretty standard stuff but the edge cases around touching endpoints tripped me up a bit.
  • Given a list of potentially overlapping time intervals (as [start, end] pairs stored in arbitrary order), write a function that merges all overlapping or touching intervals and returns the smallest possible sorted list of non-overlapping intervals covering the same range.

“My first instinct was to just sort by start time and walk through, which is the right move.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026
Amazon behavioral loop for a Data Scientist role. One question, but it was a beast, basically five questions stitched into one with very specific expectations around dates, artifacts, and process changes.
  • Walk me through a specific time you missed a deadline and a separate time you went beyond your formal responsibilities. For each: what were the exact dates and measurable impact, what trade-offs did you consciously make, which stakeholders did you loop in and when, what did your recovery plan look like with leading indicators you tracked, and what process changes would have prevented the problem or accelerated the positive outcome?

“This question wrecked me a little.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026
Amazon data scientist interview that went deep into imbalanced classification metrics. The whole session was basically one long applied ML problem with four parts, and the math gets uncomfortable fast if you haven't touched precision-recall curves recently.
  • Given 12 scored examples with a severe class imbalance (about 1% positive rate), compute precision, recall, and F1 at thresholds of 0.90, 0.60, and 0.50.
  • Which threshold maximizes F1 on this dataset, and why might business cost considerations push you toward a different threshold anyway?
  • Explain when PR curves are more informative than ROC curves, and what a comparison of AUPRC versus AUROC would tell you in an extreme class imbalance setting.
  • If you can only surface exactly k=2 alerts, compute precision@k and recall@k for the top-2 scored examples, and explain how model calibration affects your ability to use a fixed score threshold in practice.

“The arithmetic itself isn't bad but I kept second-guessing which examples fall above each threshold boundary.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon BI Engineer interview with a SQL-heavy technical screen. One question, pretty focused, all about window functions and ranking logic over a filtered date range.
  • Given a table of city-level book sales, write a SQL query that returns the top 3 books per city by total units sold, but only looking at the most recent 3-month window in the data. Output should include city, book identifier, total units sold, and rank.

“The date filtering part is what tripped me up a bit.”

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Amazon·Software Engineer·Online Assessment (OA)

Jul 2026
Amazon data engineer coding round, two problems back to back. First one was pretty manageable, second one had me sweating a bit.
  • Given an array of words, map each letter to its Morse code representation and return the count of distinct concatenated Morse translations across all words.
  • Given a string and a word dictionary, return all possible ways to segment the string into valid dictionary words, where words can be reused.

“Straightforward once you realize you just need a set.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE interview, technical phone screen focused entirely on a single tree data structure problem. The question had a lot of moving parts and I felt like I was barely keeping up by the end.
  • Design a LockingHierarchy data structure on a rooted tree of N nodes. Support lock(x), unlock(x), and isLocked(x) operations, where lock(x) only succeeds if the node and all its ancestors and descendants are currently unlocked. Each operation should run in O(log N) or better.

“This one wrecked me a little.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026
Amazon Data Scientist technical screen, one meaty SQL problem that covered window functions, retention logic, and revenue ranking all in one shot. The question was well-constructed and felt like something you'd actually write on the job, which made it slightly more stressful than a pure puzzle.
  • Given a game_sessions table and an ad_impressions table, write a SQL query that returns one row per user with their first play date, a 7-day retention flag, total ad revenue in the first 7 days from their first session, their country, and a dense rank of users within each country by that revenue figure.

“This one had a lot of moving parts and I almost forgot to handle the case where a user has zero ad impressions.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Phone screen for a Data Engineer role at Amazon. The interviewer went straight from intro to resume deep-dive, then pivoted into NLP and annotation questions that felt more ML-adjacent than pure data engineering.
  • You have a specific protocol listed on your resume. Can you explain what it is, how it works at a high level, and what your personal contribution was?
  • Walk me through a particularly difficult NLP problem you tackled. What approach did you take, why that approach over others, and what did the results look like?
  • Describe a time you collaborated with an annotation or labeling team. What problems came up and how did you handle them?

“This is the part where you realize how much you've forgotten about something you actually built.”

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