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Amazon interview notes, page 140 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

Jul 2026
Amazon technical screen, probably for a data science or ML-adjacent role. Just the one question but it's the kind that sounds straightforward until you're actually in it trying to remember all five assumptions on the spot.
  • What are the assumptions underlying linear regression, and what are the consequences if those assumptions are violated?

“I could rattle off linearity and independence fine, then kind of fumbled through homoscedasticity.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE coding round with one algorithmic problem. Pretty clean problem once the key insight clicks, but I spent way too long trying to simulate operations before stepping back and thinking about it differently.
  • You have an integer array. In one operation, you can pick any contiguous subarray and add a positive integer to every element in it. The final array must be non-decreasing. Minimize the total sum of all values added across all operations.

“Spent probably 10 minutes trying to think about greedy subarray coverage before I realized you don't need to simulate anything.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE interview with a geometry/sorting problem. Pretty standard coding round, nothing too wild.
  • Given a list of points on a 2D plane, find the K points closest to the origin.

“My first instinct was to sort by distance and call it a day, which works but they pushed on the time complexity pretty fast.”

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Amazon·Software Engineer·Onsite - Behavioral / Leadership

Jul 2026
Round 5 at Amazon for a software engineering role, two behavioral questions back to back. Pretty standard LP-style stuff but worth logging the specifics.
  • Tell me about a time you worked on continuous improvement or optimization in your work.
  • Describe a situation where you took on responsibilities that went beyond your defined role or scope.

“I had a decent story ready for this but I rambled a bit on the setup and ran out of time to land the impact clearly.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

Jul 2026
Amazon behavioral loop for a Data Scientist role, pretty much a full leadership principles deep dive from start to finish. Nothing technically crazy but the storytelling pressure is real.
  • Walk me through your background and professional journey, then take me through a recent project end-to-end with your specific contributions called out.
  • Describe a specific situation where you demonstrated a core leadership principle. Walk through the context, what you did, and the measurable outcome.

“I'd practiced this but still rambled.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026
Amazon data scientist interview that went deep into data engineering territory pretty fast. The whole thing centered on a single scenario about incremental loads and deduplication, which sounds straightforward until you're actually in it trying to explain idempotency on the spot.
  • Walk through how you'd design an incremental daily load process for a large relational table. What steps would you take, what problems came up, and how do you detect whether a specific row has already been loaded?

“This one sprawled in ways I didn't expect.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Amazon SWE interview with a scripting problem that looked straightforward on the surface but had a lot of edge cases to juggle. The kind of question where you think you're done and then they keep pulling threads.
  • Write a POSIX-compliant shell script that takes a username and a file path as arguments and outputs a single line formatted as '<timestamp> <license> <user@hostname>'. The script should validate both arguments, compute an ISO 8601 UTC timestamp, derive a license string from the file contents, handle file errors and non-ASCII paths, return meaningful exit codes, and include a basic test outline.

“I started with the argument parsing and exit codes because that felt concrete, but I got tripped up on the license extraction part.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for a PM role at Amazon. Just one question but it's a classic Amazon one so you know it's going to come up in some form.
  • Describe a situation where you anticipated what a customer needed before they asked for it.

“I had a decent story ready but I spent too long on the setup and barely got to the actual impact.”

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Amazon·Software Engineer·Technical Phone Screen

Jul 2026
Did an Amazon SDE Intern technical phone screen, about an hour long, with a project deep dive followed by two coding problems. Came out of it not really knowing what to think since one question went well and the other I never even got to code.
  • Walk me through your recent projects, including any AI or production work you've done.
  • Given piles of candies and k children, find the maximum number of candies each child can receive if every child must get the same amount.
  • In a grid where you can move to the next column only if the destination cell has a strictly greater value, find the maximum number of moves you can make.

“They went pretty deep.”

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Amazon·Data Scientist·Technical Phone Screen

Jul 2026
Phone screen for a Data Scientist role at Amazon, focused pretty heavily on causal inference. One meaty technical question that took up most of the time.
  • Explain Propensity Score Matching: what it is, what assumptions it relies on, how you'd implement it step by step, and how you'd check whether covariate balance was achieved after matching.

“This question is deceptively wide.”

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