Director QA

Experience Requirements

• 5+ years in QA Strategy & Leadership
  - Enterprise QA strategy, governance, stakeholder management, budgeting, and large team leadership.
• 10+ years in Quality Engineering & Test Architecture
  - Test automation, Performance and scalability testing, CI/CD, API/UI, microservices, test data, and non-functional testing.
• 3+ years in AI-led QA Transformation - AI-driven automation, GenAI tools, QA accelerators, and ROI-focused quality initiatives.
• 10+ years in QA Processes & Retail Domain
  - Agile/SAFe delivery with strong retail business understanding.
• 10+ years in People Leadership
  - Organization building, hiring, mentoring, competency development, and capability transformation.
• 10+ years in QA Tools & Technology
  - Test management, CI/CD, API, performance, cloud, and observability platforms.
• 7+ years in Executive Communication
  - Executive reporting, business-focused quality metrics, and senior stakeholder management.
• 10+ years of experience in SDLC, Agile, and Shift-Left/Continuous Testing.

• Hands on experience in developing utilities using AI
• Karate Framework for API testing

We are seeking a Director
- Testing (QA) who is responsible for defining and leading the organization's quality assurance and testing strategy. This role oversees test planning, automation, quality processes, test environments, tools, and testing teams to ensure high quality software releases. The Director collaborates with engineering, product, operations, and business stakeholders to drive quality across the software development lifecycle.

• Lead and manage the end-to-end Quality Assurance function across multiple projects and programs.
• Define and implement enterprise‑level test strategies, quality processes, and governance standards.
• Establish scalable automation frameworks and ensure optimal test coverage across functional, regression, API, and integration testing.
• Collaborate with Product Owners, Business Stakeholders, Development Teams, and Program Managers to define quality objectives and release readiness criteria.
• Manage test planning, estimation, execution, reporting, and defect triage activities.
• Monitor quality metrics, identify risks, and implement mitigation plans to ensure successful project delivery.
• Mentor and lead QA engineers, fostering a culture of continuous improvement and engineering excellence.
• Ensure seamless integration of automated testing into CI/CD pipelines.
• Drive production quality initiatives and contribute to reducing defect leakage through proactive quality engineering practices.
• Provide regular quality status updates and recommendations to leadership and key stakeholders.

• Strong leadership and team management capabilities
• Excellent stakeholder and client‑facing communication skills
• Strategic thinking with a quality‑first mindset
• Strong analytical and problem‑solving skills
• Ability to influence cross‑functional teams and drive decisions
• Effective conflict resolution and negotiation skills
• Strong organizational and planning abilities
• Ability to work in a fast‑paced, dynamic environment
• Mentoring and coaching skills
• Ownership, accountability, and results‑oriented approach

Success in this role over the first 6‑12 months means establishing a unified, automation‑first QA strategy that is consistently applied across programs, with Cypress frameworks fully integrated into CI/CD pipelines to deliver reliable regression, API, and integration coverage while reducing manual effort; quality metrics and dashboards drive leadership decisions with defect leakage reduction, cross‑functional teams align on clear quality objectives embedded into sprint planning and release cycles, multiple strategic initiatives achieve on‑time, defect‑minimized releases, QA engineers are mentored into automation‑first practitioners with elevated skills in Cypress, Type Script, and API testing, and proactive risk identification and mitigation practices are embedded to ensure business programs progress without major quality‑related disruptions and increase in AI adoption.