QA Engineer [gn] (Automation & Resilience)
ActianThe QA Automation function is the ultimate guardian of our platform’s reliability, scalability, and deployment velocity. In a complex, data-heavy ecosystem, manual testing is a bottleneck; continuous, intelligent, and bulletproof automation is the only way forward.
We are looking for a highly technical, hands-on QA Engineer who will focus relentlessly on End-to-End (E2E) test automation and production resilience. You are not a passive test-case writer or a mere script executor; you are a proactive engineer who thrives on breaking complex distributed systems and finding edge cases before our customers do. Your mission is to write robust automated test suites, embed them into our deployment pipelines, and ensure that our platform remains rock-solid and stable in production.
Core Responsibilities:
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End-to-End Test Automation: Design, write, and maintain robust automated E2E test suites that mimic real-world enterprise workflows on the Actian Data Intelligence Platform.
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Production Resilience Testing: Build automated tests specifically designed to test the platform's stability, load capacity, and recovery under stress or failure conditions.
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Pipeline Integration: Embed your automated test suites seamlessly into our CI/CD pipelines (GitHub Actions/Jenkins) to provide developers with rapid feedback on every code change.
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API & Data-Integrity Testing: Move beyond basic UI testing by writing deep integration and API automated tests to verify data accuracy across our distributed architecture.
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Defect Triage & Regression Fixes: Partner closely with the Sustaining Engineering team to analyze production bugs, quickly writing automated tests to reproduce and permanently block them from returning.
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Flakiness Eradication: Treat flaky tests as a critical bug. Actively track, isolate, and rewrite unreliable tests to maintain total confidence in our automation results.
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Early Quality Collaboration: Work side-by-side with backend developers and product owners early in the lifecycle to map out automated testing strategies before the code is even written.
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AI-Powered Testing Tools: Leverage modern AI assistants and coding tools to generate test data, accelerate script writing, and debug test failures efficiently.
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Clean Test Code Architecture: Write clean, modular, and reusable test scripts, conducting peer reviews with fellow QA engineers to keep the test codebase healthy.
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Data-Driven Quality Tracking: Monitor feature-level QA metrics like test execution times, code coverage, and pass rates to continuously optimize the testing pipeline.
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