Software Engineer

Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world’s biggest brands.

About the Role

We are seeking a Senior Software Engineer to join the Performance Engineering team for Data Connectors & High-Scale Ingestion. You will own the throughput, latency, scalability, and resource efficiency of multi-source ingestion pipelines that move data from SaaS, cloud, and on-premises sources into the platform at very large scale. 

In this role, you will write performance tests, generate realistic datasets, build tooling that helps the team locate bottlenecks, and ship fixes and new features that improve the scalability of the product. You will also work customer-reported performance issues through to root cause and resolution. 

What You’ll Do

• Design and run performance and scalability tests for connectors and ingestion pipelines (throughput, latency, concurrency, soak), with dataset generators that reproduce realistic source data shapes, object counts, payload sizes, and change rates 

• Debug the full stack end to end (Go runtime, OS, network, datastores, Kubernetes, cloud) to root-cause bottlenecks and regressions such as goroutine leaks, lock and channel contention, GC pressure, connection pool exhaustion, TCP/TLS behavior, API rate limiting, and slow queries 

• Analyze production workloads and telemetry in Prometheus and Kibana to find bottlenecks, and build proofs of concept that become product improvements 

• Build tooling that speeds up bottleneck analysis, such as automated pprof and trace capture and run-to-run regression comparison 

• Fix performance issues and implement new features in product code, including parallelism and pipelining, batching, back-pressure handling, and connection and memory reuse 

• Tune PostgreSQL, Redis, and Databricks usage on the ingestion path, and optimize resource usage and autoscaling of ingestion workloads on Kubernetes across AWS and Azure 

• Own customer-reported performance issues through root cause and resolution, in partnership with Support and Engineering