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Google interview notes, page 163 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 data engineer interview with two back-to-back technical problems: a SQL analytics question and a linked list merge. Nothing too exotic but the SQL one had enough edge cases to trip you up if you weren't paying attention.
  • Given a customers table, an orders table, and a returns table, write a SQL query that returns one row per customer with total order count, total amount, latest order date, and the amount of that latest order. Customers with no orders should still appear.
  • How would you modify the query to exclude returned orders from the totals?
  • Given the heads of two sorted singly linked lists, merge them into one sorted linked list by reusing the existing nodes rather than creating new ones.
  • How would you extend the linked list merge to handle k sorted lists instead of two?

“This one took longer than I expected.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Interviewed at Google for what seemed like a product or program management role. Single question about handling negative user feedback and escalating it to engineering. Not a lot to go on but it's a pretty telling question about how you bridge user pain and technical priorities.
  • If YouTube users are giving consistently negative feedback about a feature or experience, how do you handle it and how do you bring that to the engineering team?

“This is one of those questions where you can go shallow fast if you're not careful.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Did a phone screen with Google and got a coding question that's basically one of the LeetCode 150. Not identical, but close enough that prepping that list seems worth it.
  • A coding problem similar to a well-known LeetCode problem from the top 150 list.

“Not a direct copy but close enough that anyone who drilled that list would recognize it.”

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Google·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Google SWE interview with a graph/topological sort problem that had a twist I didn't fully anticipate going in. The deterministic ordering requirement is what separates this from a standard topo sort, and that detail matters a lot.
  • Given n tasks labeled 0 to n-1 and a list of dependency pairs where [a, b] means a must complete before b, return the order in which all tasks should be executed such that dependencies are respected. When multiple tasks are ready to run at the same time, always pick the one with the smallest label. Return an empty list if a cycle exists.

“I jumped straight to Kahn's algorithm and felt good about it, but then they asked why my output differed from theirs on the example and I realized I was using a regular queue instead of a min-heap.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a single product sense question built around a fictional scenario. The premise was fun but the open-endedness made it harder than it sounds.
  • You discover a magical artifact that can predict the weather perfectly for the next 30 days. As a PM at Google, what would you do with it?

“I spent the first minute just kind of staring at the prompt because I kept waiting for a catch.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Google DS interview with a meaty causal inference question about a shipped product change. No A/B test existed, which made it genuinely hard. The kind of question where you realize mid-answer how many layers there actually are.
  • Engineering shipped a new version of a product meant to reduce disconnections, but there was no holdout group. How would you rigorously evaluate whether it actually worked? Walk through your choice of method, identification assumptions, how you'd handle confounders, and how you'd communicate uncertainty to stakeholders.

“This one wrecked me a little.”

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Google·Software Engineer·Online Assessment (OA)

Jun 2026
Took the Google SWE behavioral assessment, a 60-item situational judgment test rated on a five-point scale. Nothing technically hard but it messes with your head more than you'd expect.
  • On a behavioral situational judgment test with 60 statements rated on a five-point scale, how do you carefully distinguish between items that sound nearly identical but carry different meanings, avoid defaulting to Neutral too often, stay internally consistent across your answers, and manage your time if the test runs longer than expected?

“The consistency piece is what tripped me up.”

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Google·Product Manager·Onsite - Product Sense / Strategy

Jun 2026
Google PM interview with a product design question about building an inflight entertainment system under the Google brand. Pretty open-ended, which sounds fun until you realize you have no anchor and the scope can go anywhere.
  • How would you design a Google-branded inflight entertainment system?

“I spent the first couple minutes just trying to figure out what Google even wants from this space.”

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Google·Software Engineer·Technical Phone Screen

Jun 2026
Google SWE coding round, just one algorithmic problem on meeting room scheduling. Pretty standard interval problem but the optimal solution took me a minute to land on cleanly.
  • Given an array of meeting time intervals (each with a start and end time), find the minimum number of conference rooms needed to accommodate all meetings simultaneously.

“My first instinct was to sort and simulate, which works but I fumbled explaining why a min-heap on end times is the cleaner approach.”

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Google·Data Scientist·Technical Phone Screen

Jun 2026
Short technical screen, basically one question about modeling for home price prediction. Not much else to report.
  • If you were working on predicting home prices, what modeling approach would you choose and why?

“I went straight to gradient boosting and talked through why tree-based models tend to handle the mix of numeric and categorical features you get in real estate data pretty well.”

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