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Amazon interview notes, page 64 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

May 2026
Amazon Applied Scientist interview with a classic interval merging problem. Nothing too exotic but the implementation details matter more than you'd think.
  • Given an array of intervals, merge all overlapping ones and return the resulting non-overlapping intervals.

“Sort by start time first, then sweep through and merge whenever the current interval's start is within the previous one's end.”

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Amazon·Software Engineer·Technical Phone Screen

May 2026
Amazon SWE interview with a graph traversal problem dressed up as a systems/infrastructure scenario. Pretty clever framing but once you see it for what it is, it's just BFS or DFS on a dependency graph.
  • Given a map of services to the services that depend on them, and a set of services being taken offline, return all services that will be affected either directly or transitively (including the stopped ones themselves).

“The setup is a bit verbose but it's basically graph reachability.”

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Amazon·Software Engineer·Online Assessment (OA)

May 2026
Amazon OA for a SWE role. One algorithmic problem about finding the minimum subsequence length that preserves the same sum of absolute differences between consecutive elements. Pretty niche problem, felt like it was testing whether you could see through the logistics-flavored wrapper to the underlying array manipulation.
  • Given an integer array representing machine capacities, determine the minimum number of elements you need to keep in a subsequence such that the sum of absolute differences between consecutive elements stays the same as the original. If no smaller subsequence achieves the same sum, return the original array length.

“The logistics framing threw me for a bit longer than it should have.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Amazon Data Scientist behavioral loop, two heavy leadership principle questions back to back. The whole thing felt like they wanted a dissertation on every answer.
  • Tell me about a time you voluntarily took on work that was outside your official role. Walk through the context, who was affected, how you weighed the risks, how you got your manager on board, how you balanced it against your existing workload, what you actually did, and what the measurable result was.
  • Describe a conflict you had with a teammate or an advisor. What caused it, what options did you consider, how did you handle the escalation or de-escalation, how did you keep the project on track, and what would you do differently?

“This one wrecked me a little.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Technical screen for a Data Scientist role at Amazon. The whole thing was basically a deep dive into BI tooling internals, Tableau architecture, and how it maps to QuickSight. More niche than I expected for a DS interview.
  • In Tableau, what are the trade-offs between a Live connection and an Extract when your fact table has over 100 million rows? Walk through refresh cadence, query pushdown, extract size, and when you'd choose one over the other.
  • Explain Tableau's order of operations and how it affects the behavior of data source filters, context filters, dimension and measure filters, TOP N, table calculations, and LOD expressions like FIXED, INCLUDE, and EXCLUDE.
  • What are your go-to performance tuning strategies in Tableau? Specifically around using context filters, extract filters, table calculation cost, and the decision between denormalizing data versus using joins or relationships.
  • How do Tableau's concepts map to Amazon QuickSight? Specifically SPICE versus direct query, row-level security, and filter behavior. And what's one real pitfall you'd watch out for when migrating a Tableau workbook to QuickSight?

“I started with the obvious stuff: live hits the DB every time, extract is a snapshot.”

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Amazon·Software Engineer·Onsite - Coding / Algorithms

May 2026
Amazon SWE onsite with two meaty design-heavy coding problems back to back. Both had follow-ups on complexity and trade-offs, which is where things got interesting and occasionally uncomfortable.
  • Design a data structure for an ad-matching service that supports adding a campaign, removing a campaign, and matching an incoming request to eligible campaigns. Campaigns have targeting predicates (like location, device type, interests) and budgets. How do you optimize for fast matching given frequent updates, and what are the complexity trade-offs?
  • Given a weighted graph of roads with nonnegative edge weights, a depot, and N delivery stops, implement a function to produce a near-optimal delivery route. Walk through exact versus heuristic approaches, analyze the complexity of each, and discuss how you'd handle real-world constraints like vehicle capacity or time windows.

“This one took me a while to even frame properly.”

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Amazon·Product Manager·Onsite - Behavioral / Leadership

May 2026
Amazon PM interview with a behavioral question tied to their leadership principles. Not much to it content-wise, just the one question shared.
  • Tell me about a time you convinced someone to take action they were initially reluctant to take.

“Classic Amazon leadership question.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Amazon behavioral loop for a Data Science role, focused entirely on leadership principles. Three questions, all requiring specific stories with measurable outcomes. Felt more like a values audit than anything technical.
  • Tell me about a time you disagreed with a senior stakeholder and how you resolved it.
  • Describe a situation where you failed to meet a goal. What did you learn and how did you prevent it from happening again?
  • Give an example of taking a calculated risk that had significant impact. How did you weigh the alternatives?

“This one tripped me up a bit because my instinct was to pick a story where I 'won' the disagreement.”

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Amazon·Data Scientist·Onsite - Behavioral / Leadership

May 2026
Behavioral loop for a Data Scientist role at Amazon, heavily focused on leadership principles and team dynamics. Every question tied back to collaboration, scope, or customer obsession in some way. Felt more like a project retrospective than a traditional interview.
  • Tell me about a time you disagreed with a teammate. What was the conflict and how did you resolve it?
  • Describe a situation where you delivered work that went beyond the original project scope. Why did you do it and what happened?
  • Walk me through a time you had to deliver under serious time pressure. How did you figure out what to prioritize and how did you execute?
  • Can you give an example of a project where you invented or simplified something? What unexpected insight did it surface for the customer?
  • For that same project, how many people were involved and what were everyone's roles?
  • How did you gather useful feedback from the customer and actually incorporate it into later iterations of the work?

“I went with a story about a modeling approach disagreement, where I thought we were overcomplicating the feature engineering and my teammate disagreed.”

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Amazon·Data Scientist·Technical Phone Screen

May 2026
Amazon stats interview, probably for a data or analytics role. Two questions, both about t-tests. Short but the follow-up is the one that actually matters.
  • What assumptions does the t-test make?
  • When would you use a different statistical test instead of a t-test?

“Ran through the usual list: normality, independence, roughly equal variances for the two-sample version.”

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