QA Automation Lead [gn] Data Intelligence

End-to-End Testing Infrastructure: Design, scale, and maintain robust automation frameworks capable of testing the complex SaaS and distributed data architectures of the Actian Data Intelligence Platform.

Pipeline Integration: Embed automated test suites seamlessly into the CI/CD pipeline (GitHub Actions/Jenkins) to achieve true continuous integration and rapid feedback loops.

Advanced Testing Topologies: Move beyond basic UI testing. Drive deep integration, API, performance, resilience, and data-integrity automation across the entire product stack.

Technical Anchor: Act as the ultimate technical authority for the QA automation squad, establishing clean code standards for test scripts, conducting rigorous code reviews, and minimizing framework technical debt.

Upskilling & Accountability: Coach junior automation engineers and guide functional QA testers in transitioning toward automated practices, ensuring everyone owns their automation targets.

Resource Optimization: Allocate engineering capacity effectively across feature testing, framework enhancement, and technical debt reduction.

Release Gatekeeper: Own the final quality sign-off for platform releases. Have the technical authority and conviction to halt deployments if quality thresholds are not met.

Cross-Functional Bridge: Collaborate closely with Product Management, Core Engineering, and DevOps to understand new features early, mapping out automation strategies before the code is written.

Defect Triage Collaboration: Partner with the Sustaining Engineering team to analyze production leaks, quickly writing automated tests to reproduce bugs and guarantee they never regress.

Flakiness Eradication: Treat flaky tests as a critical system anomaly. Track, isolate, and eliminate them to maintain absolute trust in the automation suite.

Actionable ROI: Track and report on key QA operational metrics (Test Coverage, Automation Pass Rates, Execution Time, and Defect Leakage Rate) to optimize delivery speed.

AI-Driven Testing: Pioneer the usage of modern AI tools to accelerate test case generation, optimize test suites, and intelligently predict defect hot-spots.

Technical Background: Strong background as a Senior SDET (Software Development Engineer in Test) or QA Automation Lead, with proven experience testing complex enterprise SaaS, distributed systems, big data engines, or data platforms.

Automation Mastery: Deep expertise in coding languages (Java, Python, or Go) and modern testing frameworks (e.g., Playwright, Cypress, Selenium, or custom-built frameworks). Exceptional SQL skills and API testing expertise are mandatory.

Extreme Ownership: Exceptional proactive behavior. You do not wait for code to be delivered to think about testing. You anticipate regressions, challenge architectural assumptions, and aggressively push for built-in quality.

CI/CD & Infrastructure Savvy: Hands-on experience with Docker, Kubernetes, cloud platforms (AWS/Azure/GCP), and version control workflows (GitHub). You understand how infrastructure impacts application stability.

AI & Innovation Adaptability: Savvy on using AI assistants and generative AI tools to write smarter test suites, mock data, and analyze test failures efficiently.

Communication: Exceptional verbal and written English communication skills. Ability to stand your ground on quality metrics while maintaining collaborative alignment with engineering leadership.