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

You will lead quality engineering for Veeam Data Command Platform, owning the strategy, teams, and automation that make every release reliable, secure, and ready for enterprise scale. Your teams will test the Knowledge Graph, the hyperscale data lake integrations, and the Universal Connector Framework. You will own quality for integrating Veeam backup workloads into the platform.

This role is for a hands-on quality leader who treats test automation as a core engineering discipline, understands backup and resilience deeply, and can build an AI-first quality culture where automation and agentic tools are a standard part of how teams ship.

What You’ll Do

Set the quality vision and strategy across Data Command Platform, including test architecture, release criteria, and quality metrics that teams can act on
Build and lead QA and SDET teams by hiring, coaching, and developing managers and senior engineers, with clear expectations and growth paths
Own quality for the integration of Veeam backup workloads, including validating backup, restore, replication, and recovery workflows and how backup data and metadata flow into the platform
Define resilience testing standards, including failure injection, chaos testing, disaster recovery scenarios, and recovery time and recovery point objective (RTO/RPO) validation
Drive test automation at every layer unit, API, integration, end-to-end, performance, and security built into CI/CD pipelines with fast, reliable feedback
Build scalable test frameworks and environments for connectors across SaaS, IaaS, and AI platforms, including test data management, service virtualization, and contract testing for third-party APIs
Lead performance, scale, and reliability testing for the Knowledge Graph and data lake integrations, including large-volume scans, graph queries, and ingestion pipelines
Build an AI-first quality practice where teams use tools like Claude Code, Cursor, and Copilot to generate, maintain, and triage tests, with guardrails that protect quality and security
Create reusable “Claude skills” and agent workflows that standardize test patterns, test generation, and failure analysis
Own release readiness, escaped-defect analysis, and continuous improvement in partnership with engineering, product, and support

What You’ll Bring

12+ years in software quality engineering, test automation, or software development, including significant hands-on SDET experience
6+ years of quality engineering leadership experience, including leading through managers and scaling multiple teams
Hands-on experience testing backup, recovery, or data resilience products and workloads; experience with Veeam products is a strong plus
Strong understanding of backup and resilience concepts, including snapshots, replication, immutability, RTO/RPO, and ransomware recovery
Proven experience building test automation frameworks from the ground up in Python, Go, and/or Java
Experience testing distributed systems, REST/GraphQL APIs, and cloud platforms (AWS, Azure, Google Cloud) at scale
Strong background in CI/CD and test infrastructure, such as GitHub Actions, Jenkins, Kubernetes, and containerized test environments
Experience with performance, load, and chaos testing tools (e.g., k6, JMeter, Locust, Chaos Mesh)
Practical experience using AI-assisted development tools and the judgement to set standards that keep AI-assisted testing reliable and secure