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Google interview notes, page 21 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·Technical Program Manager·Onsite - Behavioral / Leadership

Apr 2026
Interviewed for a TPM role at Google and got hit with a question about executive communication that I thought I was ready for. Turns out there's a difference between knowing how to pitch and actually structuring it for senior stakeholders.
  • How would you pitch an idea to senior leadership, and what would your slide deck actually contain?

“I went straight into slide structure and lost the thread on the 'why should they care' part.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Google SWE behavioral round, just one question but it was a weird one that I hadn't really prepped for.
  • In the recent past, which areas did you deliberately choose to improve in, and which areas did you consciously decide not to focus on improving?

“The second half of this question is what trips you up.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Google SWE coding round with a string grouping problem. Pretty clean problem once you figure out the key insight, but I spent a bit too long second-guessing the modular arithmetic before committing to an approach.
  • Given an array of strings, group them by their 'shift distance signature', where the signature is the sequence of modular differences between adjacent characters (delta = (ord(s[i]) - ord(s[i-1])) mod 26). Strings with identical signatures belong in the same group.

“The mod 26 part is where I fumbled initially.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE coding question involving 2D matrix traversal. Pretty niche problem, not your typical graph search.
  • Given a 2D matrix where each cell contains a character representing a color, count the total number of squares (of any size) whose four corners all share the same color.

“My first instinct was to brute force it by checking every possible top-left and bottom-right pair, which works but is slow.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Google SWE interview focused entirely on sorting, starting with a standard in-memory bucket sort and then scaling it up to a distributed external sort scenario. The follow-up was where things got interesting and where I probably showed my gaps.
  • Implement bucket sort on an in-memory array. Walk through your bucket selection strategy, how you sort within each bucket, and what the average vs worst-case complexity looks like.
  • What if the input is too large to fit in memory? How would you redesign bucket sort to handle data stored on disk, and how would you think about parallelizing it?

“Felt fine on the mechanics but stumbled a bit explaining worst-case.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with one of those product sense questions that sounds fun until you're actually in it. They asked me to design a direct competitor to my favorite product, which is deceptively hard because you have to argue against something you presumably love.
  • Pick your favorite product and design a direct competitor to it.

“I froze for a second because I picked a product I actually care about and then had to immediately argue against it.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Got a product strategy question for a Google PM role, just the one question, no fluff. Short but the kind of thing that can spiral if you're not careful.
  • How would you grow Duolingo's impact or scale by 10x?

“My first instinct was to throw out user numbers and talk about new markets, which felt too obvious the moment I said it.”

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Google·Software Engineer·Onsite - Product Sense / Strategy

Apr 2026
Google business analyst interview with a case question about diagnosing delivery time variance across cities. Pretty standard operations-meets-analytics format, though the question had enough layers to trip you up if you jumped to conclusions too fast.
  • Food delivery times vary significantly across cities. How would you figure out whether the root cause is a restaurant partner issue, an internal process issue, or something specific to the market?

“My first instinct was to go straight to the data, which I think was fine, but I spent too long on the setup and not enough time actually differentiating between the three buckets.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Interviewed for a PM role at Google and got hit with an estimation question about daily breaths. Pretty standard product sense / analytical warmup type of thing.
  • Estimate how many breaths a person takes in a single day.

“I blanked for a second and then just started anchoring on breaths per minute.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Interviewed for a SWE role at Google, and this was one of those behavioral-ish questions that sounds easy but kind of stops you cold if you haven't thought about it.
  • What is a project you are most proud of?

“I had an answer ready but it felt too rehearsed the moment I started talking.”

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