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Google interview notes, page 46 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
A product strategy question for a PM role at Google, framed around autonomous vehicles with Waymo taken out of the picture. Pretty open-ended and forces you to actually think about market sizing, regulatory environments, and competitive positioning from scratch.
  • You're the PM for autonomous land vehicles at Google (Waymo doesn't exist). How do you decide which market segment and geography to enter next?

“The Waymo constraint is doing a lot of work here.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Brutal Google DS case interview focused entirely on a single metric decline scenario for Google Meet. Six sub-questions, all connected, and they clearly expected you to go deep on every one. Left feeling like I'd passed maybe half of it.
  • Google Meet's enterprise adoption rate dropped from 38% to 31% over four weeks. How would you precisely define and validate that metric, covering event definitions, deduplication, bot filtering, cross-device identity, time zones, and attribution windows? How would you backfill and reconcile with historical dashboards?
  • Walk through a 24-hour triage plan to figure out whether this adoption drop is an instrumentation bug, normal seasonality, or a real behavioral shift. What specific queries or checks would you run, and what sanity ratios or holdout benchmarks would you use?
  • Propose at least three falsifiable hypotheses for the decline, segmented by customer tier, geography, client platform, and feature usage like recording or meeting size. What data would confirm or rule out each?
  • Design experiments to reverse the adoption decline. Cover primary and secondary metrics, guardrails, minimum detectable effect, statistical power, sample size, duration, ramp plan, and how you'd choose between variants when traffic is limited.
  • How would you isolate the causal impact of any intervention from external shocks like holidays or a competitor product launch? Walk through how you'd use difference-in-differences, synthetic control, or interrupted time series, and what assumptions each method requires.
  • What risks like metric gaming, delayed conversions, or Simpson's paradox could cause you to declare a false win? How would you monitor for these and ensure results are reproducible?

“I spent way too long on the 'what is adoption rate' part and not enough on the reconciliation piece.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google data scientist interview with a statistics-heavy question about causal inference and regression. The question was framed as a practical scenario but quickly went deeper than I expected.
  • You have data from 1,000 residents of a single city and want to study whether attending college affects income. Is a simple linear regression with a binary college variable appropriate here? Walk through the sampling concerns, whether the model is valid, and what better approaches might look like.

“I jumped straight into talking about regression assumptions and kind of glossed over the sampling issue, which was probably the more interesting part.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
PM interview at Google, one question about picking a north star metric for Google Photos. Pretty lean on details but it's the kind of question that sounds straightforward until you're actually in it.
  • What should be the north star metric for Google Photos?

“I went straight for DAU and the interviewer kind of just waited.”

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Google·Data Scientist·Technical Phone Screen

Apr 2026
Google data scientist interview with a product analytics case about reviews and sales. The question had a lot of moving parts and I don't think I covered all of them as cleanly as I should have.
  • The product team wants to understand how customer reviews affect sales. How would you design an analysis or experiment to measure the relationship between review metrics like average rating and review volume and actual sales performance?

“I jumped straight into regression and kind of glossed over the causation problem, which I think was a mistake.”

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Google·Software Engineer·Technical Phone Screen

Apr 2026
Google SWE interview with a tree iterator problem that looked manageable until the follow-up hit. The core question was interesting enough but the concurrency angle at the end is where things got real.
  • Implement a stream iterator for binary tree level-order traversal. The iterator should have next() returning all node values for one full level, and hasNext() returning whether more levels exist. Optimize for single-step time complexity and space efficiency.
  • Follow-up: if multiple iterators share the same underlying tree structure, how do you ensure correctness and performance?

“BFS with a queue is the obvious move and I went there immediately, but the tricky part is structuring it so next() only processes one level per call rather than the whole tree upfront.”

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Google·Business Analyst·Onsite - Product Sense / Strategy

Apr 2026
Interviewed at Google for what seemed like a market sizing or consulting-style role. One question, classic estimation format, nothing else to go on.
  • How many beds will be sold in the UK next year?

“Market sizing.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Behavioral round at Google for a software engineering role, basically one big question about how you handle working in the dark. Not a lot of back-and-forth, just you and the ambiguity question for a while.
  • Tell me about a time you had to make progress when things were unclear, whether that was vague requirements, changing goals, or not enough information to go on. How did you handle it, what did you give up, and how did it turn out?

“I had a decent story ready but fumbled the trade-offs part.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview with a classic business scenario question about handling hypergrowth. Pretty open-ended, which I wasn't fully prepared for.
  • You're the owner of a supermarket that just experienced 500% revenue growth. What do you do next?

“I went straight into operational scaling and completely forgot to question whether the growth was even sustainable.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Google PM interview, one question about designing a shared rides product similar to Uber Pool. Pretty open-ended and I wasn't totally sure how deep to go on the technical side versus the product side.
  • How would you design a shared rides feature, similar to Uber's carpooling product?

“I started with the user problem which felt right, but I spent too long on rider personas and the interviewer nudged me toward the matching logic pretty early.”

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