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Google interview notes, page 134 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·Software Engineer·Hiring Manager Screen

Jun 2026
Interviewed for a bizops role at Google, came away thinking the questions were more strategic than I expected for an ops position.
  • How do you mitigate risk on high-visibility projects?

“I fumbled the opener a bit, went straight into a generic answer about stakeholder alignment and then realized halfway through I hadn't actually grounded it in a real example.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with one meaty product strategy question about evaluating a major ranking algorithm change. Not a lot of context given but the question itself has a lot of surface area to cover.
  • An engineer proposes a significant change to the ranking algorithm. How would you evaluate whether to move forward with it?

“My first instinct was to jump straight into experiment design but I caught myself and backed up to ask what problem the engineer is actually trying to solve.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google DS interview that was basically a long spatial statistics case study. The whole thing revolved around one big open-ended problem about bird species distribution, but it had enough sub-parts that it felt like four separate questions back to back. Harder than I expected for a DS role.
  • How would you operationally define spatial segregation between bird species, and what quantitative metric would you use to measure it?
  • What is the correct null hypothesis for a segregation test on this observational dataset, and why is a naive 'completely random placement' null inappropriate here?
  • How would you obtain a p-value for your segregation metric, and how do you handle the multiple comparisons problem across species pairs?
  • Walk through how your approach handles uneven species abundance, habitat confounding, spatial scale differences, sampling bias, and rare species with very few observations.
  • If the coordinates are plot centroids rather than true point locations, which of your proposed methods break down and which still work?
  • If two species remain negatively associated even after controlling for habitat and survey effort, what additional evidence would you need before concluding there is active avoidance rather than an unmeasured environmental gradient?
  • The dataset spans multiple seasons. How would you incorporate observation timestamps so that seasonal species turnover doesn't get misread as spatial segregation?
  • If the ecology team wants a single summary number representing overall forest segregation, how would you responsibly aggregate results that vary across spatial scales and species pairs?

“I went straight to nearest-neighbor stats, which felt right.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a SWE role. Just the one question about conflict, which is pretty standard but still tripped me up more than I expected.
  • Tell me about a time you disagreed with someone and how you resolved it.

“I had a story ready but halfway through I realized it made me sound more stubborn than collaborative.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Google for a software engineer role. The problem was meaty enough that I kept second-guessing whether I was going too deep on certain parts or not deep enough on others.
  • Design a multi-tenant file storage service that supports upload, retrieval, and deletion, with deduplication of identical files, strict user isolation, no database allowed (filesystem APIs only), content-addressed storage, reference counting, garbage collection, and support for scaling across multiple storage nodes.

“This is a beast of a question and I did not pace myself well.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a software engineering role. Just one question but they really want you to sit in it for a while, not just rattle off a quick story.
  • Walk me through the most significant challenge you faced in a project, academic or professional. What made it hard, what did you do about it, and how did it turn out?

“I had a story ready but I rambled way past two minutes and could feel myself losing the thread halfway through.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, just one question about Maps. Short and focused, which somehow made it harder to prepare for mentally.
  • How would you improve Google Maps?

“Classic product sense question but Maps is so big that scoping it down is half the battle.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview question about how you'd respond to Amazon launching free photo storage as a competing product. Short and sharp, one strategic case question with no fluff around it.
  • You're the PM for Google Photos. Amazon just announced free unlimited photo storage. What do you do?

“This one is deceptively open.”

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Google·Technical Program Manager·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a TPM role. Just one question but they really sat with the answer, lots of follow-ups.
  • Tell me about a time you realized a project you were already deep into had a significant inefficiency. What did you do about it?

“I had a decent story ready but fumbled the middle part where I explained what I actually changed.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with an interactive coding problem. The problem was a word-guessing game where you had to find a hidden word within 10 guesses using positional match feedback, which sounds straightforward but has some real design complexity underneath.
  • Design a strategy to find a hidden word from a candidate list within 10 guesses. After each guess, you receive the number of positions where your guess matches the secret word exactly. How do you approach this?

“The interactive part tripped me up more than I expected.”

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