The opportunity
Amplitude is the leading AI analytics platform, helping over 4,700 customers—including Atlassian, Burger King, NBCUniversal, and Square—build better products and digital experiences. With powerful AI Agents embedded across our platform, teams can analyze, test, and optimize user experiences faster than ever.
What you'll do
Contribute across Nova's query execution engine and distributed compute: layer: query planning, columnar storage formats, encoding and compression, caching, and cluster-level resource management.
Implement new capabilities as Nova expands to support more warehouse-imported: data types, such as metrics, profiles, and dimensions.
Help ensure Nova's components support high-throughput automated query: workloads — as AI agents become a primary source of queries, build for sustained, concurrent, and programmatic query patterns at scale.
Own and execute projects that reduce infrastructure cost: compute, storage, network, and memory — while maintaining or improving latency and throughput.
Profile and optimize JVM performance: GC tuning, memory management, concurrency, and data layout decisions.
Build guardrails and observability to catch expensive or pathological queries before they impact the system.
What they're looking for
- Gets energy from working inside a complex distributed system: understanding how data flows through it, where the bottlenecks are, and how to make it meaningfully better.
- Has hands-on experience building or extending distributed data systems: query engines, columnar storage, streaming or batch data processing frameworks, storage engines, or equivalent.
- Thinks about cost, performance, and reliability as interconnected concerns, not separate workstreams.
- Communicates clearly about technical tradeoffs and is eager to grow your: influence through the quality of your work and ideas.
- Enjoys learning from teammates: and helping others through pairing, design reviews, or explaining the "why" behind a system's design.
- + years of industry experience in backend or infrastructure engineering, with: exposure to distributed data systems.
- Hands-on experience building or extending distributed data systems: query engines, columnar storage, large-scale data processing frameworks, streaming systems, storage engines, or equivalent.
- Experience improving cost or performance on cloud infrastructure (compute, storage, network).
