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Google interview notes, page 26 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·Technical Phone Screen

Apr 2026
Two coding problems at Google for a software engineer role. The first was a recursive expression parser, the second was a graph/BFS chain reaction problem. Pretty standard algorithmic stuff but the nesting in problem one tripped me up more than I expected.
  • Implement a function that parses and evaluates a string of nested arithmetic calls like add(1, sub(1, 0)), supporting arbitrary nesting depth.
  • Given a list of bombs each with a position and blast radius, return the maximum number of bombs that can chain-explode if you choose exactly one bomb to detonate first.

“I went straight for recursion and it mostly worked, but splitting the arguments was where I fumbled.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a PM role at Google and got hit with a classic estimation question about Street View storage. Pretty standard product sense / analytical round, nothing too wild.
  • How much storage does Google Maps' Street View require?

“I blanked for a second on where to even start.”

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Google·Product Manager·Hiring Manager Screen

Apr 2026
Interviewed for a PM role at Google and got asked about DevOps. Not exactly what I was expecting for a product interview.
  • What is DevOps?

“Wasn't sure how deep to go here for a PM role.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Went through the technical screens for a Product Data Scientist role at Google and wanted to share the format since it's a bit different from what I expected. Two technical rounds covering SQL plus A/B testing and then ML, and the HR screen is more serious than people give it credit for.
  • A key product metric shifted. How do you determine whether it reflects a real effect or is just noise?
  • SQL questions involving window functions, joins across multiple tables, and aggregations.
  • ML round focused on model evaluation and how it connects to product decisions, no implementation required.
  • How would you explain a data insight to a non-technical stakeholder?

“This is the kind of A/B testing question where you can go in a dozen directions and somehow still feel like you missed the point.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a bizops role at Google and got hit with a pretty open-ended strategic question about the product portfolio. Short loop, one question that stuck with me.
  • If you were in charge, which Google products would you shut down or exit from?

“I fumbled the opener on this.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE interview focused on a file system tree problem. Started straightforward but the follow-ups pushed into memoization and iterative DFS territory, which is where things got interesting.
  • Given a file system represented as a tree where nodes are either files (with a byte size) or directories (with children), compute the total size of the entire file system rooted at a given node.
  • How would you optimize the solution if total-size queries are made repeatedly on the same tree?
  • How would you handle cache invalidation when nodes are inserted or deleted, and would you consider switching to an iterative DFS for very deep trees?

“The base case was fine, recursive DFS summing up file sizes as you go.”

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Google·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Google SWE system design round, one question, pretty focused session. The problem sounds deceptively simple but there's a lot going on under the hood once you start thinking about ordering guarantees at scale.
  • Design a system that logs messages in the order they were received.

“I jumped straight to a distributed queue setup and then realized mid-explanation that ordering across multiple producers is actually the tricky part.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Interviewed at Google for what seemed like a security-focused product or engineering role. One question, pretty open-ended, and I left unsure if I'd framed it the right way.
  • How would you go about cutting down the time it takes to resolve security vulnerabilities across a suite of software products?

“I jumped straight into process stuff like triage pipelines and severity classification, which felt right in the moment but probably missed the bigger picture.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview, one question about designing a smart refrigerator. Pretty open-ended product sense exercise, the kind where you can either go deep on a specific user or just ramble about features for twenty minutes.
  • How would you design a smart refrigerator?

“I started by trying to narrow the user segment and almost immediately went too broad.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
A Google Data Scientist interview that came down to a single statistics question about hypothesis testing. Pretty focused and short, but the kind of question that trips you up if your fundamentals are rusty.
  • You have exactly one numeric observation. How would you test whether it was drawn from a standard normal distribution? Walk through the null and alternative hypotheses and explain how you'd make a decision.

“Felt weirdly simple at first and that made me second-guess myself.”

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