About OvalEdge

OvalEdge is an enterprise-grade AI-powered Data Intelligence platform that unifies Data Catalog, Data Governance, Data Quality, and Analytics into a single, AI-native SaaS product. Trusted by Fortune 500 companies globally, OvalEdge helps organizations discover, govern, and operationalize their data assets at scale — accelerating data-driven decisions while ensuring compliance and trust. Our platform delivers both cloud-hosted SaaS and on-premises deployments to meet the diverse infrastructure needs of enterprise customers.

The Role

We are looking for a stronger Senior QA Manager candidate profile — to own quality engineering for the OvalEdge product platform. This is a hands-on leadership role: you manage a team and get into the weeds. You write test plans, debug automation suites, design AI evaluation pipelines, run performance reviews, and lead sprint QA planning.

You come from a product development organization. You know what it means to ship a SaaS release with enterprise SLAs and support customers running on-premises deployments. You treat quality as a shared outcome, not a handoff at the end of a sprint.

And when release pressure builds and corners are being cut, you are the person in the room who holds the line — calmly, with data, in terms leadership can act on. Professional courage on quality is a requirement of this role, not a personality bonus.

What You'll Do

Quality Strategy & Ownership

- Define and own the end-to-end QA strategy for the OvalEdge platform — spanning functional, regression, integration, performance, security, and AI/LLM testing.
- Establish release quality gates and acceptance criteria in partnership with Engineering and Product; quality sign-off is a release gate, not a formality.
- Build and maintain a quality metrics dashboard — escaped defects, automation coverage, defect density, test cycle time — and drive continuous improvement against these targets.
- Own the test workplace strategy for both SaaS and on-premises deployment configurations; ensure parity between environments.
- Implement test frameworks for planning, tracking, reporting and analytics.
- Adopt AI and leverage it to the maximum extent possible for efficient test preparation, execution, analysis and reporting.
- Own quality risk communication to leadership — when a release carries unacceptable risk, surface it early, frame it in business terms, and be prepared to defend the position.

Hands-On Testing & Automation

- Remain hands-on: write, review, and debug test cases, automation scripts, and test frameworks alongside the team — this is not a purely managerial role.
- Design and evolve a scalable test automation framework (Playwright)



integrated into CI/CD pipelines for continuous regression coverage.
- Lead API, integration, and end-to-end testing across REST APIs (RESTAssured, Postman etc.), and third-party connectors — including OvalEdge's MCP-based enterprise integrations.
- Establish performance and load testing practices (JMeter, Gatling, or equivalent) for both SaaS and on-premises deployments at enterprise scale.
- Drive shift-left testing — embed QA practices in design and development, not just the testing phase.

AI & GenAI Testing

- Build and own the AI testing strategy for OvalEdge's GenAI, Agentic AI, and RAG-powered capabilities — this is a first-class deliverable, not an afterthought.
- Implement LLM evaluation pipelines using frameworks such as DeepEval, RAGAS, TruLens, and Promptfoo; define metrics for hallucination rate, faithfulness, context precision, and answer relevance.
- Design agent behavior test suites — validate multi-agent orchestration, failure recovery, and goal completion under adversarial conditions.
- Establish AI-specific quality gates in CI/CD: automated LLM regression tests that block releases when quality thresholds are breached.
- Stay current on AI testing trends and tooling; evaluate and adopt emerging frameworks as the platform evolves.

People Leadership & Team Development

- Lead, mentor, and grow a team of QA engineers and automation engineers; conduct structured 1:1s, career conversations, and performance reviews.
- Build a technical career ladder for the QA function — clear expectations at each level — so growth is transparent and retention improves.
- Define team goals, ownership, performance measures, development plans, and succession plans.
- Hire for the team: own the QA hiring process including JD definition, technical assessment design, and interview calibration.
- Champion a culture of quality ownership across Engineering — QA is everyone's responsibility, not a separate gate at the end.
- Champion automation-first approach within the team using tools, including AI, and upskill the team.

Cross-Functional Collaboration

- Partner with the Senior Enterprise Architect on quality architecture standards — test strategy aligns with the platform's broader engineering and AI roadmap.




- Work closely with DevOps to integrate test automation into CI/CD pipelines and maintain reliable, fast feedback loops for every commit.
- Collaborate with Product Management on acceptance criteria, edge case coverage, and enterprise customer scenarios — especially for data governance and compliance use cases.
- Engage with Customer Success to close the feedback loop on production issues — field signals should drive test coverage improvements before problems recur.
- Partner with Security on application security testing, VAPT coordination, and secure SDLC practices.

What You'll Bring

Required

- 12+ years in software quality engineering within a product development organization.
- 3+ years in a QA leadership role managing a team of engineers, with direct ownership of quality strategy for a commercial software product.
- Experience in hiring, goal setting, performance management, mentoring, and career development.
- Mandatory hands-on experience: must be able to write, debug, and review test cases and automation scripts — this is a player-coach role.
- Proven experience testing both SaaS (multi-tenant, cloud-hosted) and on-premises enterprise software deployments.
- Strong automation engineering skills — Selenium, Playwright, Cypress, or equivalent — with working knowledge of Python or Java.
- Experience with API testing tools (Postman, RestAssured) and CI/CD integration (Jenkins, GitHub Actions, or equivalent).
- Strong experience in Deep AI testing is a must. Demonstrated experience building or implementing AI/LLM testing practices — including familiarity with evaluation frameworks such as DeepEval, RAGAS, or equivalent.
- Strong grasp of enterprise software quality — data correctness, security, multi-tenancy isolation, performance at scale, and compliance.
- Demonstrated ability to advocate for quality at the leadership level — including the professional courage to push back on release timelines or scope when quality thresholds are not met, backed by data and articulated as business risk, not personal preference.

Nice to Have

- Experience testing Data Catalog, Data Governance, Data Lineage, Analytics, or Data Management products.
- Exposure to performance testing tools (JMeter, Gatling, k6) for enterprise-scale SaaS workloads.
- Familiarity with security testing (OWASP, VAPT coordination) and SOC 2 / ISO 27001 compliance testing.
- Experience with observability and monitoring tools (Datadog, CloudWatch, Grafana) as part of production quality feedback.
- ISTQB Advanced or equivalent certification.

Education

- Bachelor's degree in Computer Science, Engineering, or a related field (required).
- Master's degree or equivalent professional certification (preferred).

📌 Senior QA Manager (Hyderabad)
🏢 OvalEdge
📍 Hyderabad