Netflix
Streaming platform with a data-driven culture and freedom & responsibility ethos.
3 Rounds
~14 Days
Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Engineer
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Behavioral
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medium
Netflix culture heavily emphasizes 'Farming for Dissent'. Tell me about a time you strongly disagreed with a senior engineer or manager regarding a data architecture decision. How did you handle it and what was the outcome?
#Conflict Resolution
#Communication
#Netflix Culture
Data Engineer
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Behavioral
•
medium
Netflix operates on the principle of 'Context, Not Control'. Describe a project where you were given a high-level business problem with highly ambiguous technical requirements. How did you navigate this?
#Ambiguity
#Ownership
#Stakeholder Management
Data Engineer
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Behavioral
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medium
Tell me about a time you discovered a critical bug in a production data pipeline that no one else had noticed. What steps did you take to resolve it, and how did you communicate the impact?
#Freedom and Responsibility
#Incident Management
#Integrity
Data Engineer
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Coding
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hard
Write a SQL query to identify 'binge-watchers' on Netflix. Define a binge-watcher as a user who has watched 3 or more episodes of the same series within a rolling 24-hour window.
#Window Functions
#Self Joins
#Time-series Data
Data Engineer
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Coding
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medium
Given a massive log file of CDN access logs, write a Python generator function to extract specific HTTP 5xx error codes and aggregate them by region without loading the entire file into memory.
#Python
#Memory Management
#Generators
#Data Parsing
Data Engineer
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Coding
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medium
Given an array of user viewing intervals represented as [start_time, end_time], write an algorithm to find the maximum number of concurrent viewers at any given time.
#Sweep-line Algorithm
#Sorting
#Arrays
Data Engineer
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Coding
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medium
Write a SQL query to find the top 3 most-watched shows per country in the last 30 days. If there is a tie in watch hours, rank them alphabetically by show name.
#Window Functions
#Ranking
#Aggregations
Data Engineer
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System Design
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hard
Design a real-time data pipeline to process video playback events (play, pause, buffer, stop) from millions of concurrent client devices to calculate real-time viewing metrics and feed the recommendation engine.
#Kafka
#Apache Flink
#Stream Processing
#Event Sourcing
Data Engineer
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System Design
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hard
Design the data model and ETL pipeline for Netflix's A/B testing platform. Data scientists need to query experiment results via Trino with sub-second latency. How do you structure the data?
#Dimensional Modeling
#OLAP
#Trino/Presto
#ETL
Data Engineer
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System Design
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hard
Design a batch ETL pipeline to aggregate daily billing and subscription data for millions of users. How do you ensure exactly-once processing and idempotency in case of pipeline failures and retries?
#Idempotency
#Batch Processing
#Data Quality
#Airflow
Data Engineer
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System Design
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hard
Design a system to ingest client-side telemetry data (e.g., UI clicks, scroll depth, hover times) from the Netflix UI. How do you handle schema evolution when UI engineers frequently add new tracking fields?
#Data Ingestion
#Schema Evolution
#Avro/Protobuf
#Kafka
Data Engineer
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Technical
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hard
You are joining a massive fact table of viewing history (billions of rows) with a dimension table of user profiles. The user profile table is highly skewed because a few default profiles have millions of hits. How do you optimize this Apache Spark job?
#Apache Spark
#Data Skew
#Broadcast Joins
#Salting
Data Engineer
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Technical
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medium
Explain how Apache Iceberg handles schema evolution and hidden partitioning compared to traditional Hive tables. Why is this critical for managing Netflix's petabyte-scale data lake on AWS S3?
#Apache Iceberg
#Data Lakes
#AWS S3
#Table Formats
Data Engineer
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Technical
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medium
In a streaming pipeline calculating hourly active users, how do you handle out-of-order events and late-arriving data caused by offline mobile downloads syncing later?
#Event Time
#Watermarks
#Late Data Handling
Data Engineer
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Technical
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easy
Explain the difference between repartition() and coalesce() in Apache Spark. If you are writing final output data to S3 to be queried by Athena, which would you use and why?
#Apache Spark
#Shuffling
#Small Files Problem
#AWS S3
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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