Quality Assurance - Team Lead in Trivandrum | ITJOBCELL
QUALITY ASSURANCE - TEAM LEAD
0 Applied
9
Trivandrum
0 - 0 Years
Not disclosed
Quality Assurance - Team Lead
Today
Job Description
We're looking for a QA Team Lead to own quality across our product delivery lifecycle — from test planning through to release sign-off. This is a hands-on leadership role: you'll manage a team of QA Engineers, build and run a disciplined testing process, and act as the key quality checkpoint between Engineering, DevOps, and Product. You'll be equally comfortable coaching your team through a tricky defect triage and standing in front of stakeholders to explain why a release isn't ready yet.
We're a process-driven organisation with clearly defined quality gates, defect SLAs, and release readiness standards. You don't need to build this from scratch — but you do need to be excellent at running it, improving it, and holding the line when it matters.
What You'll Do
Team Leadership
Lead, coach, and grow a team of QA Engineers — including workload planning, performance management, and skill development.
Own hiring and onboarding for the QA team, including training new hires on our testing tools and quality standards.
Run regular team retrospectives and turn recurring issues into concrete process improvements.
Represent QA in sprint planning, backlog grooming, design reviews, and release meetings.
Flag resourcing, tooling, or skills gaps to your manager early, backed by data (defect trends, coverage gaps, missed SLAs).
Set the team's day-to-day rhythm: sprint-level test planning, defect triage, and monthly quality reporting.
Quality Process Ownership
Own the end-to-end test planning process: requirements review, test strategy, test case design, test data preparation, execution, defect logging, retest, and sign-off.
Make sure QA is involved from the start of each sprint — test coverage should be planned before development begins, not bolted on afterward.
Keep core QA deliverables current every sprint: test plans, test cases mapped to acceptance criteria, test data, the regression suite, the defect log, and a release readiness summary.
Run the required test types every sprint — functional and regression testing as standard, smoke testing after every deployment, integration testing for API changes, and performance/security testing for major releases.
Jointly own the Definition of Done with the development team — a story doesn't move forward until both development and QA have signed off.
Own the handover checklist into user acceptance testing, confirming the technical side is ready before business stakeholders get involved.
Maintain a clean, well-documented defect lifecycle — clear titles, severity ratings, reproduction steps, expected vs. actual behaviour, and environment details on every ticket.
Enforce defect response times based on severity, escalating critical issues immediately and routine ones on a predictable cadence.
Make sure nothing gets closed without being properly retested, and that visual or design-related issues get sign-off from the design team before being dismissed.
AI-Assisted Development Quality Checks
Own the extra QA steps required whenever AI tools have been used in development — reviewing security scan results, checking that AI-referenced code libraries and functions are real and behave as expected, running full regression on any AI-touched code, and confirming any resulting technical debt gets logged.
Apply the team's internal quality marker to any AI-assisted work once QA has signed off — this is what confirms the work is ready to move forward.
Make sure the team never enters proprietary code, client data, or credentials into any AI tool.
Release & Cross-Functional Coordination
Sign off on release readiness, confirming all planned work has cleared testing and no serious defects remain open.
Provide QA's security validation as part of the pre-release security review.
Confirm regression results and coverage targets are met before a release is cleared for the next stage.
Work closely with DevOps on any post-release issues, including confirming environment stability and supporting rollback decisions if something goes wrong.
Act as QA's point of contact for the design team on visual or UX-related defects, and respond quickly when a design question needs QA input.
Coordinate with Business Analysts on preparing acceptance testing scenarios.
Report on team and quality metrics to your manager on a regular cadence.
What We're Looking For
Leadership & People Skills
Proven experience managing and developing a QA team (8+ people), including performance conversations.
Confident stakeholder management — you're comfortable representing QA to Product, Engineering, DevOps, and senior leadership.
A track record of running structured retrospectives and turning quality data into real process improvements.
Process Discipline
Experience building or running a structured, repeatable QA process — not ad hoc testing.
Comfortable working with issue-tracking tools (e.g. JIRA), defect classification systems, and clear documentation standards.
Used to working within multi-step approval processes and holding a firm line on quality standards, even under release pressure.
Technical Knowledge
Solid grounding in the full testing lifecycle: functional, regression, smoke, integration, performance, and security testing.
Familiarity with security scanning tools and vulnerability/dependency checks, and how to read their output.
Working knowledge of CI/CD pipelines and environment structures (dev, QA, staging, UAT, production) and how QA fits into each stage.
Comfortable with the idea of validating AI-assisted code — checking for issues like fabricated dependencies or silent regressions. No prior experience required; we'll provide training.
Experience
10+ years in Quality Assurance, including at least 2 years leading a QA team.
Experience working in a structured, process-driven environment is a strong plus.
Skills
Solid grounding in the full testing lifecycle: functional, regression, smoke, integration, performance, and security testing.
Familiarity with security scanning tools and vulnerability/dependency checks, and how to read their output.
Working knowledge of CI/CD pipelines and environment structures (dev, QA, staging, UAT, production) and how QA fits into each stage.
Comfortable with the idea of validating AI-assisted code — checking for issues like fabricated dependencies or silent regressions. No prior experience required; we'll provide training.
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