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Google interview notes, page 37 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

Apr 2026
Google PM interview with a big open-ended strategy question. No frills, just one meaty prompt and a lot of pressure to structure your thinking fast.
  • You have 3 years to make a given product the best in the world. What strategy would you implement?

“The scope of this thing is brutal.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google data scientist interview with a machine learning fundamentals question. Pretty standard stuff on the surface but they pushed for specifics pretty fast.
  • Your model performs much better on training data than on test data. Walk through what's causing this and what you'd actually do about it.

“I started with the bias-variance framing which felt right, but I rambled a bit connecting variance to model complexity before getting to the practical fixes.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Google coding round with two related array problems. The follow-up was the real test and I'm not sure I handled it cleanly.
  • Given an array of integers, find the length of the longest strictly increasing contiguous subarray.
  • Follow-up: you can change exactly one element in the array. What is the longest increasing contiguous subarray you can achieve?

“Pretty standard sliding window type problem.”

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Google·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed for a bizops role at Google, and it was pretty much a single pointed question about my consulting background.
  • Why are you leaving consulting?

“Knew this was coming and still fumbled it a bit.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google interview, data or analytics role, one question about a forecasting model that flopped in production. Short session, felt more like a diagnostic than a full loop.
  • Walk me through a forecasting model you built where the real-world results didn't match what the model predicted.

“This is the kind of question that sounds easy until you're actually in it and realize every example you're reaching for either makes you look incompetent or is too vague to be useful.”

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Google·Product Manager·Onsite - Behavioral / Leadership

Apr 2026
Google PM interview, one behavioral question about handling a difficult technical stakeholder. Short and to the point, but not easy to answer well.
  • Tell me about a time you had to work with a difficult technical stakeholder.

“I went with a story about an eng lead who kept pushing back on timelines without much explanation.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Got a stats question at Google that I wasn't fully prepared for. Short interview, one question, left me second-guessing my answer the whole way home.
  • What is the purpose of a Weibull distribution and when would you use it?

“I knew the textbook answer but fumbled trying to give a practical example on the spot.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Google with a product strategy question about Airbnb's north star metrics. Short on details but the question itself is a solid one worth thinking through.
  • What should Airbnb's key north star metrics be?

“This is the kind of question where you can go in five different directions and none of them feel totally wrong.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Google SWE coding round with a pretty gnarly string/subsequence problem that took me a while to even parse correctly. The problem statement alone felt like a puzzle before I even touched the algorithm.
  • Given a string s, determine whether every subsequence of s with length 3 or more can be rearranged into a valid English dictionary word. Return true if this holds for all such subsequences, false otherwise.

“I spent way too long just re-reading the problem.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google data scientist interview with a coding-heavy technical screen built around a payment reconciliation tool. The problem had multiple parts and pushed into edge case territory pretty fast.
  • Given a list of invoices and payments, implement ID-based matching: parse the invoice ID from a payment memo that contains a specific prefix, look up the invoice, and return a structured match result or an error if the invoice doesn't exist.
  • Extend the matching logic so that payments without the memo prefix fall back to amount-based matching: find all invoices with the same amount, and if there are multiple, pick the one with the earliest due date. Return structured results for all payments.
  • How would you design the output structure for the reconciliation results, covering matched, unmatched, and error cases across both matching modes?

“The memo parsing tripped me up more than I expected.”

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