Domino Data Lab is a company that builds software for AI-driven organizations to enhance data science and AI solutions at scale. The Staff Performance Quality Engineer will own and modernize the scale testing platform, ensuring reliability and efficiency while supporting the growth of complex products and cloud infrastructure.
Responsibilities:
- Serve as the technical owner of Tempest, Domino’s scale testing platform tools, ensuring they remain reliable, extensible, and aligned with evolving product needs
- Evolve and modernize scale testing infrastructure to support continued growth and increasing product complexity
- Deliver accurate, data-driven sizing recommendations for customer-facing documentation based on rigorous empirical testing
- Dramatically improve validation and reporting within scale testing by introducing repeatable, automated validation criteria that reduce manual diagnostics while increasing confidence in results
- Establish and operationalize scale testing on cloud platforms, ensuring appropriate sizing and configuration guidance for this increasingly divergent product line
- Partner with platform teams to enable effective scale testing across additional cloud providers, helping position Domino for future multi-cloud success
- Increase the efficiency and leverage of a small team by building automation that scales operationally as the product and customer base grow
Requirements:
- Experience owning or extending complex automation platforms (e.g., performance/load testing, end-to-end frameworks, CI systems)
- Strong Python skills and the ability to work effectively in an automation-heavy codebase
- Hands-on experience with distributed systems, reliability engineering, and scaling cloud-based infrastructure
- Familiarity with Kubernetes and modern cloud-native tooling
- Demonstrated ability to design reliable, multi-stage test automation systems that produce actionable results
- Clear ownership mindset. Ability to be self-directed, accountable, and able to communicate priorities and status effectively