[ OK ]2e5849e1-a267-4acc-9a4a-26383c7f44f3 — full writeup
[ INFO ]category: Behavioral · Multiple Types difficulty: 6 freq: first seen: 2026-01-30
[6][MULTIPLE TYPES]System DesignData ModelingTopological SortBehavioral QuestionsProduct Sense
$catproblem.md
Netflix — Software Engineer ✅ Passed
Level: Senior-Level
Round: Full Journey · Type: Multiple Types · Difficulty: 6/10 · Duration: 240 min · Interviewer: Unfriendly
Topics: System Design, Data Modeling, Topological Sort, Behavioral Questions, Product Sense
Location: Los Gatos, CA
Interview date: 2025-05-15
Summary
Round 1: System Design
Question: Designed an ads frequency cap. This differs from a regular rate limiter because read and write paths are separate due to bidding. Reading the frequency cap doesn't guarantee an ad will be served, so writing happens in the event tracking portion.
Round 2: Data Modeling
Question: Data modeling for ad demand, including campaign settings.
Round 3: Coding
Question: Solved a topological sort problem. I don't remember the exact problem context.
Round 4: Behavioral (BQ)
Question: Standard behavioral questions, with a focus on product sense. My examples emphasized this point.
Details
Preparation Tips & Key Takeaways
What I Learned
I realized the importance of understanding product sense when interviewing for product-focused companies.
I also learned that I should be prepared to explain my experience with data modeling, particularly in the context of ad tech.
Recommended Preparation
System Design
Review rate limiter implementations and their variations.
Understand the nuances of ad frequency capping.
Data Modeling
Study ad demand concepts and campaign settings.
Consider using ChatGPT to learn the basics if you don't have prior experience.
Coding Practice
Practice topological sort problems.
Behavioral Questions
Prepare examples that showcase strong product sense.