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Amazon interview notes, page 2 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 - Multi Round

Apr 2025
Four rounds for an SDE 1 role at Amazon across about a month, covering DSA, LRU cache with OOP follow-ups, leadership principles, and an AI discussion. Rounds went reasonably well by the candidate's account but the result has been radio silence for over a month after the final round, with only a generic recruiter reply and a survey to show for it.
  • Implement an LRU cache and discuss the object-oriented design and relevant design patterns behind it.
  • Have you used AI or machine learning in any of your projects, and how did you approach it?
  • Describe a time you did something particularly difficult and how you handled it.
  • Tell me about a time you received negative feedback. How did you respond to it?

“Got the core implementation down fine but the follow-up questions on design patterns felt a bit more open-ended than I expected.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2025
Applied for an SDE-1 role at Amazon, got shortlisted, and completely bombed the online assessment. Two questions, zero solved, and a week's worth of anxiety leading up to it didn't help.
  • Solve a DSA problem as part of the coding challenge round.
  • Read and work with an unfamiliar codebase to complete a repository-based coding task (AI tools permitted).

“Couldn't get it.”

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Amazon·Software Engineer·Onsite - Multi Round

May 2025
Cleared round 1 for an SDE 2 role at Amazon and now staring down two back-to-back rounds with almost no prep time and zero clarity on what order they're happening in. The recruiter herself wasn't sure whether the Bar Raiser or the LLD round comes first, which made prioritizing basically impossible.
  • What does the Bar Raiser round actually cover for a senior SDE role? Is it really a mix of system design, coding, and leadership principles all in one session?
  • Is the low-level design round always a standalone session, or can it come up inside the Bar Raiser round as well?
  • With under a week to prepare across LLD, HLD, data structures, and leadership principles, how do you actually prioritize?
  • Has anyone navigated an Amazon interview pipeline where the order of rounds was genuinely unclear, and how did you handle it?

“This was my main source of panic going in.”

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Amazon·Software Engineer·Online Assessment (OA)

Jun 2025
Amazon OA question for a SWE role, apparently a pretty new problem that took a while to get fully documented. The problem statement was incomplete for a bit but eventually got filled in. Seems like a simulation/greedy type problem with some tricky edge cases around the skipping mechanic.
  • You and a colleague alternate dispatching goods from a series of warehouses. You always dispatch a fixed amount first, then your colleague dispatches a different fixed amount or skips his turn (up to a limited number of skips total). You score a point only when you clear the last unit from a warehouse. Given the warehouse inventories, your dispatch amount, your colleague's dispatch amount, and the total skips allowed, find the maximum number of points achievable.

“This one took me a while to even understand what was being asked.”

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Amazon·Software Engineer·Technical Phone Screen

Jun 2025
SQL-heavy BI engineer screen at Amazon, two questions back to back with some schema context provided upfront. Nothing too wild but the second question tripped me up more than I expected.
  • Write a SQL query to return the top 100 books by total quantity sold within the current calendar month, across all marketplaces.
  • Write a SQL query to find all customer IDs who bought at least one book but purchased zero magazines across the entire dataset.

“Pretty standard aggregation question.”

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Amazon·Data Scientist·Technical Phone Screen

Aug 2025
Amazon data scientist technical screen, heavy SQL and Python. One big multi-part question that took up basically the whole session. Felt like a take-home problem crammed into a live interview.
  • You have separate employee tables for different countries (US, UK, Japan). Write a single SQL query that unions them all together and returns the top 10 employees ranked by salary converted to USD, using the most recent available exchange rate per currency on or before a given date. Output should include country code, employee ID, name, original salary, currency code, and the USD salary.
  • Write a Python solution using pandas that reads all employee CSV files from a directory (matching a filename pattern), concatenates them, joins to an exchange rates CSV, computes the USD salary, and returns the top 10 employees by that value.

“This took me a while to structure cleanly.”

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Amazon·Data Scientist·Technical Phone Screen

Aug 2025
SQL-heavy technical screen for a Data Scientist role at Amazon. One question, but it had enough layers to keep me busy for the whole session.
  • Write a SQL query that computes, for a specific three-day window, the proportion of users who logged in only via mobile, only via desktop, and via both channels. The denominator should be distinct users who used at least one channel during that period. Tables are mobile_logins and desktop_logins, each with user_id and login_dt. Return counts and percentages rounded to two decimals, and explain corner cases you handled.

“This looked like a clean aggregation problem until I started thinking about the denominator.”

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Amazon·Data Scientist·Technical Phone Screen

Sep 2025
Amazon DS technical screen, heavy on experimentation. The whole thing was one big A/B testing case study with real numbers to crunch, and they pushed hard on the statistical reasoning and pitfalls side rather than just asking you to run a t-test and call it a day.
  • Given 7 days of daily exposure and purchase data from a 50/50 checkout A/B test, compute overall conversion rates for both variants, the absolute and relative lift, and run a two-proportion z-test including p-value and 95% confidence interval.
  • Check for sample ratio mismatch both day-by-day and in aggregate. If you find one, what could cause it and how would you fix it?
  • Is it appropriate to pool all 7 days together for analysis? Justify using a fixed-effects versus random-effects framing.
  • Identify at least three pitfalls in this experiment window and propose guardrails you'd put in place before launching.
  • Your MDE was a +5% relative lift. Given the data, estimate whether the test achieved sufficient power, and decide: roll out, iterate, or extend?

“This part I felt okay about.”

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Amazon·Data Scientist·Technical Phone Screen

Sep 2025
Amazon data scientist technical screen, basically one massive SQL/Python design question that took the whole session. The problem was dense enough that I kept second-guessing whether they wanted a real production answer or just a sketch.
  • You need to load the last 7 days of orders from a noisy staging feed into a large fact table. Design a full approach that handles idempotent reruns, late-arriving updates, and duplicate detection. Include DDL, a dedupe CTE using window functions or aggregation, a MERGE statement that only updates when the incoming row is newer, a watermark strategy for catching late data, row hashing for prior-load detection, and at least two data-quality assertions that can trigger a rollback.

“This was basically the whole interview compressed into one question.”

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Amazon·Software Engineer·Onsite - Multi Round

Nov 2025
A mid-level software engineer reflects on a brutal stretch of interviews after a layoff, covering everything from Amazon to a Japanese company that told them to just use AI and then rejected them anyway. The process was all over the place and left a pretty sour taste.
  • What instruction in EF Core (.NET) improves performance specifically for read-only queries?
  • Given a repository with a makefile and a list of tickets, complete the objectives using AI tools wherever applicable.
  • Solve a pen-and-paper riddle (given verbally at the end of a technical interview with roughly five minutes remaining).
  • In a timed one-hour assessment: answer 8 mixed machine learning concept and multiple-choice questions, then solve one medium and one hard LeetCode problem with the remaining time.

“This came from an interviewer who was clearly going deep on ORM internals.”

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