Location
San Jose, CA; on-site work format, with no other geographic restrictions specified.
About the company
A global data resilience and data security posture management company serving over 550,000 customers. Its platform focuses on the convergence of identity, data, security, and AI risk.
Responsibilities
- Set the quality vision, test architecture, release criteria, and actionable quality metrics for a data command platform covering knowledge graphs, hyperscale data lake integrations, and a connector framework.
- Lead QA and SDET teams, including hiring, coaching, and developing managers and senior engineers, and own quality for integrating backup workloads such as backup, restore, replication, and recovery.
- Define resilience testing standards, including failure injection, chaos testing, disaster recovery, and RTO/RPO validation, and drive automation across unit, API, integration, end-to-end, performance, and security layers in CI/CD.
- Build scalable test frameworks and environments for SaaS, IaaS, and AI platform connectors, and establish an AI-first quality practice using tools such as Claude Code, Cursor, and Copilot with reusable agent workflows.
Requirements
- 12+ years in software quality engineering, test automation, or software development, including significant hands-on SDET experience.
- 6+ years of quality engineering leadership, including leading managers and scaling multiple teams.
- Hands-on testing of backup, recovery, or data resilience products, with strong knowledge of snapshots, replication, immutability, RTO/RPO, and ransomware recovery.
- Experience building test automation frameworks in Python, Go, and/or Java, testing distributed systems, REST/GraphQL APIs, and cloud platforms at scale, plus CI/CD and test infrastructure such as GitHub Actions, Jenkins, and Kubernetes.