Senior Software Engineer

As an organization, we deliver for our customers by living our values. We operate from a place of humility, take ownership of problems and successes, approach challenges with a growth mindset, and put our customers at the center of everything we do.

Amplitude’s Commitment to Diversity Equity & Inclusion (DEI): Amplitude believes that diversity enables the creation of better products, improves the ability to solve complex problems, and drives more powerful solutions. We strive to create an environment of inclusion—one focused on psychological safety, empathy, and human connection—that will allow employees of all backgrounds to thrive.

About the Role & Team

Every AI insight, every experiment, every cohort at Amplitude starts with a query. Our in-house OLAP engine, Nova, processes trillions of events in real time — turning raw behavioral data into fast, trustworthy answers that power decisions for thousands of product teams worldwide.

Were entering a world where AI agents dont just assist product teams — they ship features, run experiments, and make prioritization calls autonomously. What makes that possible is agents ability to verify their work against real product data continuously. That makes Nova the critical infrastructure in the loop, and as non-stop agents become the main source of queries, the demand on Novas throughput, correctness, and operational rigor grows dramatically.

Were looking for a Senior Software Engineer who wants to go deep on the engine internals and the infrastructure underneath. Youll own significant components of a modern OLAP system — across query execution, columnar storage and encoding, distributed compute, caching, and cloud infrastructure — and drive meaningful improvements to performance, cost-efficiency, and reliability. Youll grow your technical influence through the quality of your code, your design contributions, and your collaboration with other engineers on a team of ~10.

This role is ideal for someone who finds real satisfaction in making a complex distributed system faster, cheaper, and more reliable — and who wants to do that work on a system that directly powers the product experience for thousands of enterprise customers.

What Youll Do

Build and improve core query engine components

• Contribute across Novas 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 Novas 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.

Improve cost and performance

• 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.

Contribute to reliability and operational excellence

• Strengthen Novas reliability posture in the components you own: identify failure modes, drive durable fixes, and raise the bar on how we detect and respond to production issues.

• Participate in on-call rotation to root-cause incidents and turn one-off fixes into lasting improvements.

• Contribute to capacity planning, safe rollout practices, and the operational tooling that keeps Nova healthy.

Grow through technical contribution

• Lead the design and execution of multi-week to multi-month projects within your area of ownership.

• Contribute to technical direction through design docs, architecture discussions, and code reviews.

• Pair with and learn from senior and staff engineers on the team; share what you learn with peers.

• Collaborate with Product, Middleware, Data Pipeline, and other engineering teams to ensure Novas capabilities translate into customer value.

Who You Are

You are a systems-minded engineer who:

• 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 systems design.

Qualifications

Requirements

• 3+ 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).

• Strong computer science fundamentals: distributed systems (partitioning, replication, consistency, failover), data structures and algorithms, concurrency and multi-threading, performance optimization.

• Production experience with modern cloud infrastructure — AWS (S3, DynamoDB, EC2), Kafka, Redis/ElastiCache, Kubernetes, Terraform — or strong equivalents.

• Proficiency in Java, C++, or Python.

• Demonstrated ability to own and ship significant pieces of complex systems, and to collaborate effectively with engineers and partner teams.

Nice to Have

• Experience with specific OLAP or query engine systems: Druid, ClickHouse, Presto/Trino, BigQuery, Snowflake, or similar.

• JVM expertise — GC tuning, profiling, memory optimization.

• Experience with columnar data formats and encodings (Arrow, Parquet, ORC, or custom formats).

• Familiarity with product analytics, experimentation platforms, or event-driven data systems.

• Contributions to open-source data infrastructure projects or published work in the data systems space.