Quality Analyst

QA Engineer – Insights (AI/ML)

Qualifications

• 3–5 years of software quality assurance experience spanning both manual and automated testing.
• Hands-on test automation experience across UI and API layers using tools such as Playwright, Cypress, Selenium, Postman, or REST Assured.
• Strong SQL skills and the ability to independently validate data rather than relying solely on application-level reporting.
• Scripting or programming proficiency, ideally in Python or JavaScript, sufficient to build and maintain an automation framework.
• Demonstrable CI/CD experience, including integrating automated tests into pipelines, implementing quality gates, and supporting environment promotion.
• Experience testing REST APIs, including contract testing, authentication, error handling, and asynchronous workflows.
• Experience with test planning, test-case design, defect management, and release sign-off.
• Working knowledge of Git and branch-based development workflows.
• Strong written communication skills, with the ability to create clear defect reports and test documentation that others can act on.

• Experience testing data pipelines, ETL processes, or analytics platforms, including data reconciliation and data-quality validation.
• Experience validating machine learning or statistical model outputs, including reproducibility and model regression testing.
• Experience with Databricks, Spark, or modern lakehouse platforms.
• Performance and load testing experience using tools such as JMeter, k6, or Locust.
• Experience with cloud platforms; Azure preferred, including Azure DevOps or GitHub Actions.
• Experience testing multi-tenant SaaS applications, including tenant isolation and access-control verification.
• Familiarity with geospatial data validation and testing.
• Experience using AI-assisted coding tools such as Cursor or GitHub Copilot and/or agentic coding tools such as Claude Code as part of a professional development workflow, including authoring and maintaining automated tests.

• Experience working in a regulated industry where test evidence is subject to external audit.
• Understanding of pipeline integrity or asset-management concepts.
• Experience with security testing practices, including static and dynamic analysis tools.
• Experience validating data migrations from legacy or spreadsheet-based systems.
• Experience with accessibility testing.

Success Metrics

Success in this role will be measured by:

• Automated regression coverage across UI, API, and analytics layers that runs reliably in CI on every change.
• Low defect escape rates, with critical defects identified and resolved before reaching production.
• Data and model validation processes that identify accuracy issues before they reach customers.
• Audit-defensible test evidence maintained for every model release.
• Stable, fast, and trustworthy test suites that the engineering team relies on rather than routinely bypassing or overriding.
• Quality risks identified and communicated early enough in the development cycle to be addressed proactively.