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Google interview notes, page 166 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

Jun 2026
Google SWE interview with a tricky black-box debugging problem. The kind of question that looks deceptively simple until you actually have to think about the search space.
  • You're given a black-box function that takes a list of test cases and either passes or fails. It's known that exactly two specific test cases in the list cause a failure when both are present together. How do you find that pair?

“My first instinct was brute force all pairs, which is O(n^2) calls to run().”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE technical phone screen, one graph problem, pretty standard stuff but the specific variant they wanted tripped me up a bit.
  • Given an n x n grid where each cell has an elevation value, water rises uniformly over time such that at time t the water level equals t. You can move between adjacent cells only when the water level covers both. Find the minimum time needed to travel from the top-left to the bottom-right cell.

“I recognized it as a graph problem pretty fast but my first instinct was BFS with binary search on the answer, which works but they pushed me toward something cleaner.”

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Google·Technical Program Manager·Hiring Manager Screen

Jun 2026
Google TPM screen, just one question about the day-to-day. Short and a bit underwhelming if I'm being real.
  • Walk me through what your day-to-day looks like as a TPM.

“Seemed like a warmup but I think I rambled.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google data scientist interview with a meaty case question about comparing two competing snack shops over a 14-day window. One question, but it unpacked into five sub-parts covering metric design, data collection, causal identification, regression modeling, and decision guardrails. Dense.
  • Two snack shops operate side by side at a school entrance. You have 14 days to figure out which one will generate more profit over the next quarter. Design a full measurement and analysis plan: define your primary metric and why it's the right one, specify the minimum data you'd collect, describe your causal identification strategy given real-world confounders like exam weeks and mid-study price changes, write out the regression you'd fit including fixed effects and controls, and state your explicit decision rule with guardrails for things like stockouts.

“This one sprawled fast.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

Jun 2026
Behavioral round at Google for a software engineer role. Just one question but they really sat with your answer, so you couldn't coast on a vague story.
  • Tell me about a time you learned something new that caused you to change how you approached your work. What sparked the learning, what did you actually change, and what happened as a result?

“I had a decent story ready but fumbled the middle part where you're supposed to explain what specifically changed.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE onsite in Munich, first NG round. The problem was a two-player tile-stack merge game called Babylon: implement the full game from scratch including legal-move detection and state transitions. Coding it cleanly in 45 minutes was rough and the interviewer had to drop hints to keep things moving.
  • Implement a two-player tile-stack merge game from scratch. The board starts with 12 height-1 stacks across 4 colors (3 tiles each). On each turn a player merges two stacks under specific legality constraints: same height OR matching top color. The player with no legal move loses. Build the full game: state representation, legal move enumeration, move execution, and terminal detection.

“This wrecked my pacing.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
A deep-dive causal inference question at Google for a DS role, framed around a music app feature and speeding. The whole thing was one long multi-part problem that kept escalating in complexity.
  • A lawyer is concerned that users who listen to a 'Commute' playlist in a mobile app drive faster. How would you define the population, unit of analysis, treatment, and outcome for a study on this, including precise time windows?
  • What are at least five major confounders in this study, and how would you measure each one?
  • Design a safe randomized controlled trial to test whether the playlist causes faster driving. Cover eligibility criteria, safety guardrails, kill switches, metrics, and stopping rules.
  • If a proper experiment isn't feasible, what quasi-experimental approaches would you use? Walk through within-driver fixed effects, staggered adoption difference-in-differences, instrumental variables using exogenous surfacing changes, and regression discontinuity on a ranking score threshold. Include identifying assumptions and falsification tests for each.
  • How do you handle the fact that treatment can start mid-trip, that compliance is partial, and that some users in the data aren't drivers at all? What data granularity do you need to avoid leakage?

“This part felt deceptively clean but I kept second-guessing myself on the unit of analysis.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Google SWE system design round focused almost entirely on security monitoring internals. No typical backend fluff, just deep low-level stuff I wasn't fully prepared for.
  • Design a security monitoring framework.
  • How would you detect attacks in a security monitoring system?
  • How does eBPF work and how would you use it in a security monitoring context?
  • How would you design a shared buffer for passing security event data between components?

“I came in expecting the usual distributed systems angle and got blindsided by how deep into the weeds this went.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, one question about measuring success for YouTube search. Pretty focused session, no fluff.
  • How would you measure success for YouTube search?

“I started with user satisfaction stuff like click-through rate and watch time after a search, then moved into query success rate and zero-result rates.”

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Google·Product Manager·Onsite - Behavioral / Leadership

Jun 2026
Went through a behavioral round at Google for a PM role. Just the one question but it was a classic self-starter type prompt that I probably underprepared for.
  • Tell me about a time you taught yourself a new skill.

“I had an answer ready but it felt thin in the moment.”

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