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Google interview notes, page 158 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

Jun 2026
Went through a behavioral round for a TPM role at Google. Just the one question but it had some real teeth to it.
  • Describe a situation where your technical judgment shaped a program in a way that caused the project scope or roadmap to change.

“This one tripped me up more than I expected.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Interviewed for a data science role at Google, got a stats fundamentals question that felt like a warmup but tripped me up more than it should have.
  • Can you explain the difference between a Z-test and a t-test, and when you would use each?

“Knew this cold in theory but fumbled the delivery a bit.”

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Google·Product Manager·Hiring Manager Screen

Jun 2026
Went through a PM screen at Google, basically one behavioral question about deadlines. Not much to report but figured I'd log it.
  • Describe a time you had to deliver a project under a tight deadline.

“Pretty standard stuff.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a classic string algorithms problem. Nothing too wild but the requirement to go beyond naive O(n^2) made it more interesting than it looked at first glance.
  • Given a string of lowercase letters, find the longest substring that appears at least twice (overlapping occurrences are allowed). Return an empty string if no such substring exists. Your solution should be meaningfully faster than a naive O(n^2) approach.

“The 'banana' -> 'ana' example is easy to see but getting to an efficient solution is where it gets real.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a software engineer role, focused entirely on team communication. One question, but it had a lot of layers and I don't think I handled the multi-audience part as well as I could have.
  • How do you keep your team aligned? Walk through a real example covering things like status updates, design reviews, async docs, or 1:1s. Also, how do you adjust how you communicate depending on whether you're talking to engineers, PMs, or leadership?

“I had a decent example ready about a cross-team migration project, status docs and a recurring sync, felt solid.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview that revolved entirely around one problem with three escalating phases. Felt manageable at first but the third variant tripped me up more than I expected.
  • Given a list of words, return the top-k or bottom-k most or least frequent words.
  • How would you modify your solution to handle a stream of incoming words and maintain the top-k or bottom-k most frequent words in real time?
  • Same streaming problem, but now each word should only be counted once regardless of how many times it appears. How does that change your approach?

“Started with a hashmap for counts, then a heap for the top-k part.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google data science interview with a stats-heavy modeling question. One round, one meaty scenario, and I left second-guessing whether I'd justified my framework well enough.
  • You have 100 reviewers each rating 100 YouTube ads on a 1–10 scale. How would you model this data to produce ad scores that account for reviewer bias, and why would a mixed-effects linear regression be the right tool here?

“I knew the answer involved separating reviewer tendencies from actual ad quality, but articulating why mixed effects specifically took me longer than it should have.”

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Google·Technical Program Manager·Hiring Manager Screen

Jun 2026
TPM interview at Google with a project recovery scenario that felt more like a stress test than a conversation. One question, but it had a lot of layers to unpack.
  • You join a project that is only 25% complete but has already consumed 75% of its budget and resources. What do you do?

“My first instinct was to jump straight into 'stop the bleeding' mode and I think that hurt me.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
PM interview at Google with a market sizing question about a car dealership. Pretty classic estimation format, nothing too wild, but I definitely fumbled some of the assumptions mid-answer.
  • Estimate the total number of tires a large car dealership in San Jose, California purchases in a year.

“I started with new car sales volume and forgot to account for service department tire replacements until pretty late.”

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Google·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for an ML Engineer role. Just one question but they really dug into it.
  • Tell me about a time you had a conflict with your manager. Walk through what the disagreement was about, how you brought it up, how it got resolved, and what you took away from the experience.

“This one tripped me up more than I expected.”

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