You'll be embedded in the engineering team of a FinOps SaaS platform, owning automated test coverage for a product where correctness isn't cosmetic — it's business-critical. Cloud cost calculations, billing logic, and usage data recommendations are what customers use to make financial decisions. If the numbers are wrong, the trust is gone. Your job is to make sure they're right, and to build the infrastructure that proves it systematically.
The platform processes large-scale cloud billing and usage data for cloud-driven organizations — turning raw consumption into cost reports, optimization recommendations, and dashboards used by finance, engineering, and FinOps teams. The domain is inherently data-heavy and logic-dense: edge cases aren't a corner of the product, they're the core of it. The test surface spans dashboards, data pipelines, integrations, and business-critical calculation layers.
Technology Stack: Automation runs on Cypress or Playwright — modern, well-supported, JavaScript/TypeScript-native. AI tooling is used actively as part of the QA workflow, not as a gimmick: test planning, coverage analysis, failure triage. The product environment is data-heavy, so expect SQL and cloud billing logic to be part of your testing surface, not just UI flows.
Design and build automated end-to-end tests for complex web application flows using Cypress, Playwright, or similar frameworks — with a focus on data-heavy product areas: reports, dashboards, recommendations, cost logic
Translate product behavior, user workflows, and technical risks into test scenarios that actually catch real issues — not just green checkmarks
Work alongside developers and PMs to understand new features before they ship, identify quality risks early, and shape what "done" means
Investigate bugs with depth: reproduce, identify root cause, document clearly, and communicate findings to the team — not just log a ticket
Use AI tooling actively to accelerate test planning, test generation, failure analysis, and coverage improvement
Maintain and improve the test suite's structure, reliability, and scalability — not just add tests on top of existing ones
Participate in release processes and act as a quality signal for what's safe to ship
2–4 years of hands-on QA automation or Software Engineer in Test experience
Practical experience building and maintaining automated tests for web applications
Experience with Cypress, Playwright, Selenium, or comparable frameworks
Solid coding skills in JavaScript, TypeScript, Python, or similar
Strong analytical thinking — you dig into why something failed, not just that it failed
Experience testing SaaS products with dashboards, reports, or data-intensive surfaces
Good English communication skills
API testing experience
Familiarity with Git and CI/CD workflows
SQL or large dataset experience
Basic understanding of AWS, Azure, or GCP
Experience testing integrations, data pipelines, billing systems, or analytics platforms
Experience improving automation infrastructure, not just writing individual tests
The product domain is technically substantive — cloud cost logic, billing data, and FinOps workflows have real edge cases that require engineering-level understanding to test well. You're not validating whether a form submits correctly.
The team is small and experienced, which means your work has visible impact and your decisions on test architecture actually stick — there's no bureaucratic layer between "you think this is better" and "this is now how it works."
AI-assisted testing is a genuine part of the workflow here, not a talking point — if you want to push what modern QA practice looks like in a real product context, there's room to do that.