Senior Automation Engineer - SDET

About Josys

Josys is a modern AI-native identity security and governance platform. Enterprise AI adoption has made identity the fastest-growing attack surface, and the hardest to govern. Josys discovers, governs, and secures every identity — human, machine, and AI agent — across every application in the enterprise, allowing security and IT teams to surface risk, control access, and remediate identity threats in real-time. Trusted by over 1,000 organizations and MSPs worldwide, Josys turns identity from the fastest-growing attack surface into an autonomously governed advantage. For more information, visit josys.com.

About the role

The Josys QE function is undergoing a strategic transition toward an automation-first, data-driven, and AI-augmented model embedded in every engineering team. This role is essential to achieving our North Star of building a world-class QE function and transitioning to a full SDET-driven delivery model.

Technologies:

Cypress, Playwright/ Protractor, Pytest framework (Python),API testing, Database (SQL/ MySQL/ Postgres/ MongoDB), Performance tools (JMeter, K6), Test trail, Jenkins, TypeScript/JavaScript, Browser Stack, MERN Stack, Microservices, Containers/Orchestration, DevOps, HTML/CSS, JavaScript, Node.js, Express, React.js.

What You Will Do

• Automation Framework: Implement, maintain, and extend the existing scalable automation frameworks for your dedicated engineering team.

• Automation Development: Drive the "Automation First" goal by independently developing automation for complex, high-value features, ensuring coverage targets are met, and providing technical coaching to peers on advanced test development best practices.

• Pipeline Execution: Implement and maintain automated quality gates (PR gate, release gate, post-deploy) within the CI/CD pipeline, ensuring adherence to governance standards.

• Advanced Testing: Apply and implement advanced testing practices, including integration, end-to-end (E2E) automation, contract tests (e.g., Pact), performance tests, and service-level resiliency/chaos tests for your team's services.

• Tooling Contribution: Contribute to the adoption and utilization of the standardized QE tooling strategy, focusing on high-quality automation and non-functional testing (NFT) execution.

• Test Data Management: Execute and maintain the established test data management standards for compliant, on-demand, prod-like test data solutions, including PII masking and leveraging tools like Faker.js.

• Shift-Left Support: Collaborate with developers, providing technical guidance to support the shift-left approach and improve the quality of unit and functional tests owned by the development team.

Required Qualifications

• 3+ years of experience in Cypress- Automation designing, implementing and supporting automated test solutions for SaaS products hosted on the cloud.

• Expertise in implementing and enhancing automation frameworks for software teams.

• Proven ability to implement automated tests for integration, E2E, and API/contract testing.

• Experience with continuous integration tools and embedding automated quality checks into CI/CD pipelines.

• Familiarity with defining and tracking key quality metrics (e.g., defect density, defect escape ratio, flaky rate).

• Experience using test case management tools (e.g., TestRail) to manage test cases and trace them to JIRA stories.

• Experience of Non-Functional Testing (NFT) concepts (e.g., performance testing, security testing) and the ability to execute existing test scripts.

• Proven experience in leveraging AI/LLM tools (e.g., Cursor, ChatGPT) to accelerate QA workflows such as test generation, defect analysis, or synthetic test data creation.

• Experience building internal bots, or developer-facing automation using AI

• Experience to monitor automation suite health and assist in root cause analysis (RCA) for flaky tests.

• Familiarity with emerging testing practices, such as knowledge of AI-augmented testing solutions.

• Familiarity with Test Data Management (TDM) concepts and experience utilizing existing synthetic data generation tools.