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

How candidates describe it

Real Software Engineer 4 - Graph Search interview stories — retold from candidates' public write-ups, with sources.

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