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Google interview notes, page 189 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·Onsite - Behavioral / Leadership

Jun 2026
Interviewed for a business analyst role at Google, one round, behavioral flavor. Pretty standard stuff but I fumbled more than I expected.
  • Tell me about a time you built a dashboard.

“I had an example ready but rambled way too much about the tool itself instead of the business problem it solved.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding round, one question with a follow-up, interviewer was chill and the whole thing wrapped up with time to spare.
  • Reverse a linked list between two given positions, then modify your solution to do it in-place.

“The base problem wasn't bad, got through it and did a dry run without too much fumbling.”

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Google·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Google for a software engineering role. The whole session was one big question about building a replay system on top of an existing pub/sub setup, and it went pretty deep across like six different dimensions. Dense but interesting.
  • Given an existing pub/sub messaging system, design a replay system that lets consumers reprocess historical messages from a specific point in time or offset, without affecting live traffic. Cover durable message retention and indexing, replay APIs, isolation from live consumers, ordering and delivery semantics, schema evolution, and crash recovery.

“This was basically six questions stapled together and I did not pace myself well.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google data scientist technical screen, heavy on the stats and ML theory side. One question that covered a lot of ground around linear models and regularization. Not a coding round, more of a 'prove it on the whiteboard' situation.
  • You fit a linear model on X1 and X2, then refit it using transformed predictors Z1 = X1 minus X2 and Z2 = X1 plus X2. First, prove OLS predictions are identical across both models for any invertible linear transformation, and show how the coefficients map between the two. Then, explain whether ridge or lasso penalties preserve this invariance, with precise conditions. For ridge, write out the solutions and show when predictions stay the same. For lasso, give a concrete counterexample where predictions actually differ.

“This wrecked me a little.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE interview with a grid-based constraint satisfaction problem. The kind of question that looks like competitive programming but has enough depth to keep you second-guessing your approach for the whole hour.
  • Given an N by M grid and k crop types, each with a required count that sums to N*M, produce a valid grid layout where every crop appears exactly the prescribed number of times and all cells of the same crop form a single 4-connected region.

“This one took me a few minutes to even understand what the constraints were actually demanding.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Product sense question for a PM role at Google, specifically framed around YouTube Music and how you'd drive massive user growth over a five-year horizon. No fluff, just one meaty strategy question.
  • You're a PM at YouTube Music. How would you grow its monthly active users by 10x over the next five years?

“The scale of it tripped me up a bit.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview, one question about bidding strategy for default search engine placement on Firefox. Not a lot of context to go on but it's a classic Google-specific strategy case that I wasn't fully prepared for.
  • What factors should Google weigh when determining how much to bid to stay the default search engine on Firefox?

“This is a pricing and competitive strategy question disguised as a simple 'what factors' prompt.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding interview that went sideways fast. Got hit with a 2D DP problem that apparently requires knowing some niche algorithm, and even after grinding through hundreds of LC problems, I wasn't ready for it.
  • Solve a 2D dynamic programming problem that requires knowledge of a specialized algorithm.

“Described the brute force approach verbally with complexity analysis but didn't code it up, then tried jumping straight to bottom-up DP and completely fell apart.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
SQL heavy technical screen for a Data Scientist role at Google. One question, but it had enough moving parts that I was mentally juggling constraints the whole time and probably talked too much before writing anything.
  • Given a schema with orders and order_items tables, write SQL to find the top 5 product pairs most frequently bought together across distinct orders. Each pair should be counted at most once per order, no self-pairs, smaller product_id listed first, ties broken by pair count then lexicographic order. Return p1_id, p2_id, and pair_count. Also suggest indexes to make this fast on large data.

“The self-join to generate pairs tripped me up at first because I kept second-guessing the less-than condition for ensuring smaller id comes first.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE interview that was basically one coding problem plus a distributed systems rabbit hole. The algorithmic part felt manageable but the scale follow-up is where things got real.
  • Given an array of intervals [start, end], find the minimum number of conference rooms needed to schedule all meetings without conflicts.
  • How would you scale the same problem if you had billions of intervals spread across many machines?

“Knew this one.”

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