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Netflix interview notes, page 1 of 20

FAANG / Big Tech

Most of these Netflix write-ups come from Software Engineers (136), then Data Scientists (23) and Product Managers (16). The most reported rounds are the technical phone screen (94), the system design round (48), the product sense round (14) and the behavioral / leadership round (11).

191 interview notes · updated Jul 2026

Netflix·Data Scientist·Technical Phone Screen

Apr 2024
Netflix data scientist round that was heavier on SQL than I expected. Two retention-focused queries plus a Python algo problem. Nothing too wild but the Day-7 retention rate query tripped me up a bit.
  • Given a transaction table, write a SQL query that returns each user's first transaction date and the count of transactions they made within 7 days of that first purchase.
  • Write a SQL query that computes the overall Day-7 retention rate across all users.
  • Write a Python function that takes a list of n integers and returns the two numbers whose sum is closest to zero. Assume the list has at least two elements.

“Used a window function to get the first date per user, then joined back on the same table filtering where the date difference was within 7 days.”

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Netflix·Data Scientist·Technical Phone Screen

Sep 2025
Netflix DS interview, technical phone screen style. One big SQL problem covering DAU, new user cohorts, D1 retention, and revenue metrics all in a single query. The bot-exclusion and dedup requirements were the kind of gotchas that feel obvious in hindsight but trip you up in the moment.
  • Given three tables (users, events, purchases), write a single SQL query returning one row per day for a 7-day window with: daily active users, new users (first event on that day), D1 retention rate for those new users, and revenue per DAU. Exclude bot users and deduplicate exact-duplicate events before aggregating.

“This is a lot to pack into one query and I fumbled the D1 retention part first.”

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Netflix·Data Scientist·Technical Phone Screen

Sep 2025
Netflix Data Scientist technical screen, one meaty SQL question that covered DAU, cohort logic, and edge cases all at once. Felt like a reasonable challenge but the zero-activity dates requirement was the part that tripped me up most.
  • Write a single ANSI-SQL query returning one row per day for the last 7 days with columns for daily active users (session events only), new buyers (users whose very first order falls on that day), and conversion rate (new buyers divided by DAU, rounded to 2 decimals, returning 0.00 when DAU is zero). Dates with zero activity must still appear.

“The 'first-ever order' part is straightforward once you think MIN(order_date) grouped by user, but I initially forgot I needed to join that back against the date spine and almost counted repeat buyers.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026
Netflix SWE coding round, one problem the whole time, a twist on the classic parentheses generation problem. Pretty standard vibe but the modification kept it from being a total freebie.
  • Given a number n, generate all valid combinations of n pairs of parentheses, with a specific modification to the classic problem.

“Knew the base problem cold so I jumped straight into the recursion.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026
Netflix ML researcher interview with a 30-minute deep dive into your own research project. Pretty intense if your work involves a lot of team collaboration because they really want to pin down exactly what you personally did.
  • Walk us through your research project in depth: what motivated it, what problem you were solving, and how it fits into the existing landscape of prior work.
  • What were the key technical decisions in your approach, and why did you make those specific choices over alternatives you considered?
  • Describe your experimental setup and results. What did the numbers actually show?
  • What were your specific individual contributions versus what the broader team did?
  • What did you learn from this project, and what would you do differently?

“This part felt fine until they asked about the gap in prior work.”

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Netflix·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Netflix SWE process, second week they decided my coding was shaky enough to throw in an extra round. The follow-up was a tree traversal problem, DFS on an org-level structure.
  • Given a tree representing an organizational hierarchy, implement a depth-first search traversal.

“They added this round because my earlier coding performance was 'mixed', which, fair enough.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Netflix ML engineer screen, just one question about TF-IDF. Short and a bit anticlimactic if I'm being real.
  • Can you explain how TF-IDF works?

“Walked through term frequency and inverse document frequency separately before tying them together.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026
Netflix SWE interview, just one question surfaced from the content but it was a follow-up that caught me a bit off guard.
  • How would you approach solving this problem using DFS? No need to code it out, just walk through the approach.

“They didn't want code, just the reasoning, which honestly made it harder in a weird way.”

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Netflix·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Netflix PM interview with a single product design question around building a referral system. Pretty open-ended, which sounds fun until you realize there's a lot of directions you can go and not much scaffolding to lean on.
  • How would you design a referral program for Netflix to help grow its user base?

“I started by anchoring on why referrals even make sense for Netflix specifically, which felt like the right move but ate up a lot of time before I got to anything concrete.”

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Netflix·Software Engineer·Onsite - Coding / Algorithms

Apr 2026
Netflix onsite coding round, one question but it had three layers to it and the interviewer kept pushing on runtimes until I could spit them out without re-deriving everything on the spot. Felt like a stress test more than a coding exercise.
  • Implement a topological sort on a DAG. Then extend it to handle tie-breaking when multiple nodes are available at the same time (e.g. by lexicographic order). Then extend it further to enumerate ALL valid topological orderings. For each variant, state the runtime explicitly.

“The base implementation was fine, I went with Kahn's and got through it cleanly.”

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