120 032
23 863
We’re building Astra Serverless, the next generation of distributed, scalable, fault-tolerant, serverless NoSQL data services — powered by Apache Cassandra and extended with native Vector and AI capabilities across multi-cloud environments. Our customers depend on our platform to serve real-time, mission-critical workloads on a global scale. Ensuring reliability, performance, and correctness under unpredictable workloads is a non-trivial challenge — and that’s where you come in. As an engineer on the Quality Engineering and Performance team, you’ll develop and evolve the system-level testing frameworks that validate a distributed database-as-a-service at massive AI-driven workload scale. You’ll help ensure that new features, performance improvements, and AI-driven extensions meet the highest standards of scalability and resilience. Why this role? You’ll work at the intersection of distributed systems engineering and test architecture — hands on designing and building automation and frameworks that simulate complex multi-cloud deployments, chaos scenarios, and performance stress conditions. This is not QA-as-usual: you’ll engineer the test systems that validate an elastic database platform capable of scaling thousands of non-uniform nodes, self-healing under failure, and integrating real-time vector search and analytics. If you thrive on deep technical challenges, curiosity, analytical and systems thinking, and building tools other engineers rely on, this role will feel like home. * Design and develop frameworks for end-to-end and chaos testing of distributed, serverless Cassandra-based systems. * Engineer automation that validates data correctness, fault tolerance, and performance under complex multi-region and -cloud topologies. * Collaborate closely with your peers in local and remote feature development teams to model real-world scenarios and integrate automated validation into the delivery pipeline. * Continuously evolve the test infrastructure for scale, speed, and observability — leveraging Kubernetes, Docker, and cloud-native toolchains. * Profile and tune distributed workloads to uncover systemic bottlenecks and verify that service-level goals are consistently met. * Contribute code to shared testing frameworks and participate in design and code reviews across teams. * Own the full cycle of quality engineering — from test design and execution to insights and continuous improvement.