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Google interview notes, page 153 of 215

FAANG / Big Tech

Most of these Google write-ups come from Software Engineers (1,167), then Product Managers (686) and Data Scientists (125). The most reported rounds are the product sense round (667), the technical phone screen (660), the behavioral / leadership round (252) and the onsite coding round (214).

2146 interview notes · updated Oct 2026

Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM strategy question at Google, pretty much a single scenario case about cloud migration in the energy sector. No behavioral warmup, they just dropped the case and let me run with it.
  • Azure is trying to grow its HPC (high-performance computing) footprint in the energy sector. How would you build a strategy to get energy clients to switch from AWS to Azure's HPC offering?

“I went straight into customer segmentation which felt right but I probably spent too long there.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a KPI design question. Pretty open-ended, which I wasn't fully prepared for.
  • How would you choose a KPI for a system you're building, and how would you go about improving it over time?

“I went straight to user engagement metrics without pausing to think about what 'the system' actually needed to accomplish.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google data scientist interview that went deep into linear algebra and high-dimensional regression. The questions were genuinely hard and felt more like a stats PhD oral exam than a typical DS screen. Left feeling like I'd passed some parts and completely fumbled others.
  • Derive the OLS estimator from scratch and explain what rank conditions are needed for it to be identifiable. What is the sampling distribution of the estimator under classical assumptions?
  • When you have more predictors than observations, what are your options? Walk through at least three regularization or dimensionality reduction approaches, and explain how you'd pick the regularization parameter and validate the model.
  • When does the Moore-Penrose pseudoinverse give you a useful minimum-norm solution, and what are the practical drawbacks of relying on it?
  • Why doesn't simply duplicating rows in your dataset fix a rank deficiency problem, and how does it affect your inference?

“I can derive this in my sleep so the algebra was fine.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a graph traversal problem dressed up as a movie recommendation feature. The core was BFS with a custom ranking on top, plus a production systems follow-up that I didn't feel super prepared for.
  • Given a movie similarity graph, a starting movie, a set of already-watched movies, and ratings per movie, return the top K recommended movies ranked by graph distance from start, then by rating descending, then by movie ID ascending.
  • What production issues could come up if you deployed this recommendation system at scale, and how would you handle them?

“The BFS part clicked fast but I fumbled the tie-breaking for a bit.”

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Google·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
PM interview at Google with at least one behavioral question that felt like it could've come from anywhere.
  • How do you prioritize tasks?

“Pretty standard stuff.”

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Google·Data Scientist·Onsite - Behavioral / Leadership

Jun 2026
Google Data Scientist interview with a meaty behavioral question that basically asked you to defend a controversial call under pressure. One question, but it had four layers and the interviewer clearly wanted specifics, not vibes.
  • Tell me about a time you inherited a struggling metric or model, disagreed with how the team wanted to fix it, and still had to make a recommendation on a tight deadline. Walk through the disagreement, how you got stakeholders on board, what guardrails you put in place, and what the results actually looked like.

“This one is deceptively long.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE coding round with a sequence-processing problem that looked manageable on the surface but had enough edge cases to keep you busy for a while.
  • Given a sequence of typed events with timestamps, find all segments where the same event type repeats consecutively at least N times, each adjacent pair is within T time units, and the total duration spans at least X. Return the count of such segments.

“My first instinct was a basic sliding window and I started coding before fully thinking through what 'segment' meant here.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, one question, product sense focus. The accessibility angle made it more interesting than a typical design prompt but also way harder to scope.
  • Design a ride-hailing app for blind users.

“My first instinct was to just list accessibility features and call it a day, which would've been a disaster.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a classic speculative product question. Pretty open-ended, which I liked but also found disorienting because there's no right answer to anchor on.
  • How would you approach launching a new teleportation product at Google?

“Spent the first minute just trying to figure out where to even start.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview that went into some interesting string processing territory. The follow-up question is the part that stuck with me.
  • Given a string that may have been 'jammed' by a keyboard (causing repeated characters), determine which dictionary words could have produced it. Then optimize so you're not scanning the entire dictionary on every query. For example, can you tell immediately that 'banana' could never match 'hheelllo'?

“The base version I got through fine.”

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