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Google interview notes, page 139 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 coding round with a string problem that looks deceptively simple until you start thinking about how many substrings you actually have to check. The core challenge is anagram validation across all contiguous substrings of length 3 or more, and the dictionary lookup angle makes it more interesting than a typical sliding window question.
  • Given a string of letters and access to an English dictionary (as a set), return true if every contiguous substring of length 3 or more can be rearranged into a valid dictionary word. In other words, for every such substring, there must exist some word in the dictionary that is an anagram of it.

“The anagram-check part is fine, sort the characters or use a frequency map, compare against dictionary entries.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, one question about a key metric dropping. Short and brutal.
  • YouTube's daily active users are declining. How do you diagnose and respond to this?

“My first instinct was to jump straight into fixes, which was probably the wrong move.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a product design question framed around elevators for skyscrapers. Pretty unconventional setup but the core ask was standard differentiation and feature ideation.
  • You're a PM at an elevator company. How would you design new features specifically for skyscrapers, and how would you differentiate from competitors?

“I spent way too long on the skyscraper context trying to sound smart about vertical transportation and kind of buried the actual product thinking.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Did a product sense round at Google for a PM role. One estimation question, classic market sizing format. Pretty short session from what I can tell.
  • How many parking spots are there in San Francisco?

“Classic estimation question.”

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Google·Product Manager·Hiring Manager Screen

Jun 2026
Interviewed for a PM role at Google and got asked about navigating work outside your comfort zone. Pretty short post but the question itself stuck with me.
  • How do you handle projects that fall outside your area of expertise?

“I fumbled this a bit.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a classic grid traversal problem. Nothing too exotic but they wanted you to actually think through multiple approaches rather than just code the first thing that popped into your head.
  • Given a 2D binary grid of '1's (land) and '0's (water), count the number of islands, where an island is a group of adjacent land cells connected horizontally or vertically. Walk through multiple approaches including depth-first search, breadth-first search, and union-find.

“I went straight to DFS because it's the one I can code fastest under pressure, which maybe wasn't the smartest move.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE coding round, one problem the whole time but it had two parts and the second part is where things got real. Part 1 felt manageable, Part 2 (digit DP) is where most people apparently run out of clock.
  • Given a definition of a 'Perfect Wake Number' (positive integer with all distinct non-zero digits and no interior digit strictly smaller than both its neighbors), first implement a function to check if a single number qualifies, then count how many such numbers exist in the range [1, n].

“Part 1 is basically a linear scan and I got through it fine.”

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Google·Product Manager·Onsite - System Design / Architecture

Jun 2026
Google PM interview with a pretty meaty system design / strategy hybrid question about migrating a legacy payroll system. One question, lots of surface area, and I definitely underestimated how deep they wanted me to go on the compliance angle.
  • Walk me through how you would design a data migration plan to move a legacy payroll system onto a new platform, covering migration phases, data mapping and validation, risk mitigation, rollback strategies, cut-over timing, and stakeholder communication.

“I started with phased vs big-bang and felt pretty confident there, landed on phased with a parallel run period which they seemed to like.”

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Google·Software Engineer·Online Assessment (OA)

Jun 2026
Google SWE online assessment, two problems in 90 minutes on HackerRank with AI assist enabled. The second problem was a feature rollout system with country/OS gating and dependency cycle detection, and it was a lot more involved than it sounds.
  • Implement a feature rollout evaluation system that gates users by country and OS version, then resolves per-feature dependencies while detecting and fully enumerating every member of any dependency cycle.
  • Given a buggy dependency manifest parser that incorrectly merges multiple dependencies into a single string entry, identify and fix the bug with a minimal code change rather than rewriting from scratch.
  • Extend an evaluate() function to handle dependency cycles of arbitrary length, ensuring the cycle report includes every node in the cycle including the entry node, not just the point where the cycle was rediscovered.
  • When a feature's dependency is itself part of the current rollout batch (not yet resolved), how should the evaluation handle it rather than auto-rejecting?

“This one wrecked my time budget.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Went through a Google coding round for a Software Engineer role and got rejected two days later. The feedback pointed to the coding session, which in hindsight made total sense once I replayed what actually happened in there.
  • Given a coding problem, walk through a brute-force solution before optimizing.

“The interviewer literally suggested starting with brute force.”

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