Netflix
Mid
SWE4, Netflix Graph Search — backend + distributed search at studio scale
Build and operate Netflix's internal Search-as-a-Service platform (Graph Search) that powers discovery across studio and production tooling, moving data from transactional systems into near-real-time search indices. The role spans backend feature ownership, distributed search cluster scaling, and the team's newer chat-based natural-language search interfaces. Core stack is Java/Python, Spring Boot, GraphQL/gRPC, Kafka, OpenSearch/Elasticsearch, AWS Neptune, Cassandra, and AWS.
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What this interview tests
- Search-as-a-service architecture and index topology at scale
- Near-real-time data pipelines from transactional to search stores
- Backend feature ownership in Java/Spring Boot
- Sharding, throughput, and data consistency for search clusters
- Graph data modeling (AWS Neptune) vs document search (OpenSearch/Elasticsearch)
- Emerging natural-language / RAG-style search interfaces
Common question themes
Design a near-real-time indexing pipeline from a transactional DB into a search index
When to use a graph database vs a search index for content relationships
Debug/optimize a sharding or throughput bottleneck in a search cluster
Own a backend feature end-to-end in Java — testing and deployment approach
How you'd extend structured search into a natural-language chat interface
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