lever
Senior Software Quality Engineer
Protolabs
Maple Plain, USA
Full-time
Senior · 7+ years experience
Hybrid
Discovered 1 weeks ago
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What you’ll do
Support and evolve the test strategy for the Pricing domain, covering functional, integration, contract, regression, and non‐functional testing such as performance, reliability, and ML quality gating across services and APIs
Design, implement, and maintain automated test suites for APIs, events, integrations, and targeted end‐to‐end and performance scenarios aligned with the team’s architecture, including API and event‐driven components and cloud deployments
Own and extend continuous testing within GitHub Actions or Azure DevOps CI/CD pipelines, ensuring test execution on PRs, enforcing quality gates, reporting results, managing flaky tests, and integrating with deployment workflows.
Validate pricing and ML outputs for correctness, edge cases, and regression, including geometry‐driven estimates, probabilistic forecasts, and model‐serving behaviour where applicable
Partner with engineers to improve testability through clear acceptance criteria, observable systems, contract testing between services, and shift‐left practices during code reviews. Investigate defects in production and pre‐production environments, perform root cause analysis, and drive preventative improvements such as closing automation gaps, increasing coverage, and enhancing processes
Contribute to release readiness by assessing risks, defining regression scope, providing go or no‐go input, and verifying system behaviour after release for pricing‐critical changes
Monitor quality metrics including coverage trends, defect escape rates, pipeline stability, and SLA‐related signals, and communicate status clearly to engineering and product leadership while automating post‐release monitoring
Mentor QA engineers and SDETs, promote testing standards, establish coding conventions for tests, and drive shared ownership of quality and testing frameworks across the engineering organisation. Participate in on‐call or incident support rotations when needed, focusing on reproductions, adding automated regression coverage, and ensuring long‐term fixes
Ensure the test approach aligns with relevant regulatory, audit, and data handling requirements, especially in the context of commercial pricing systems
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