Netflix — Software Engineer ❌ Failed
Level: Senior-Level
Round: Onsite · Type: Multiple Types · Difficulty: 6/10 · Duration: 300 min · Interviewer: Unfriendly
Topics: Data Modeling, Behavioral Questions, System Design, Topological Sort
Location: Los Gatos, CA
Interview date: 2025-02-15
Got offer: False
Summary
Round 1: Data Modeling
Question: Designing a data model for Ads demand intaking. This round might be easier for those with an advertising (especially measurement) background.
Round 2: Behavioral (BQ)
Question: Standard behavioral questions. I tried to align my answers with the company's value memo. I found questions like "How do you balance long-term and short-term work?" and "How do you prioritize your work?" particularly challenging.
Round 3: Behavioral (BQ)
Question: General behavioral questions, answered according to value memo.
Round 4: System Design
Question: Designing an ads frequency cap system, as discussed on the forum. A follow-up involved implementing an impression cap for each ad, similar to a coupon system.
Round 5: Coding
Question: A disguised topological sort problem.
Details
Preparation Tips & Key Takeaways
What I Learned
- Aligning my answers with the company's value memo is crucial for behavioral questions.
- Understanding the fundamentals of data modeling and system design for ad tech is essential.
Recommended Preparation
Data Modeling
- Review data modeling principles and best practices.
- Study data structures and algorithms relevant to data storage and retrieval.
System Design
- Practice designing systems for ad serving and frequency capping.
- Familiarize yourself with common system design patterns and architectures.
Behavioral Questions
- Prepare STAR stories that demonstrate your understanding of the company's values.
- Practice answering common behavioral questions related to prioritization and long-term vs. short-term goals.
Coding
- Practice topological sort problems.
- Review common graph algorithms.
Resources I Recommend
- Company's value memo and leadership principles.
- Online resources for system design and data modeling.
Common Pitfalls to Avoid
- Failing to align answers with company values.
- Lacking a solid understanding of data modeling and system design principles.
- Not being able to effectively communicate my thought process during the coding round.