SQA Manager

Introduction Welcome to Gallagher in India — where expertise, technology, and purpose come together. Since 2006, Gallagher in India has supported global teams by delivering quality, service, and speed through deep expertise, smart technology, and specialized knowledge services. More than just an operations center, it’s a place where careers grow through collaboration, continuous learning, and purposeful work. We drive efficiency, compliance, and innovation so our teams can focus on serving clients. If you enjoy solving problems and working with purpose, Gallagher is the place where you can grow and feel a sense of belonging. Overview Gallagher Center of Excellence is seeking a Senior QA Engineer with 10–15 years of relevant experience to lead quality engineering for Gallagher Drive analytics products. The role will define and govern the QA strategy across Power BI reports and dashboards, data and ETL pipelines, APIs, and supporting applications. The Senior QA Engineer will provide hands-on technical leadership, establish repeatable QA processes and controls, guide automation architecture using Python, PyTest, and Playwright, drive risk-based release readiness, mentor QA resources, and partner with business, product, development, data, and reporting leaders. This is a senior technical individual-contributor role; direct people-management responsibility should be confirmed separately by the hiring manager. How you'll make an impact Responsibilities: QA Strategy and Governance: Define the end-to-end quality strategy, operating model, risk-based test approach, entry and exit criteria, evidence standards, release gates, sign-off controls, SOPs, checklists, and regression governance across analytics products. Test Leadership and Assurance: Lead planning and assurance for functional, integration, system, regression, data, API, ETL, reporting, smoke, performance, security-related, and release validation; ensure appropriate coverage across critical business journeys. Power BI Quality Engineering: Establish testing standards for data accuracy, DAX calculations, semantic models, filters, slicers, interactions, drill-through, navigation, refresh behavior, role-based access, visual standards, accessibility, and performance. Data and ETL Quality Leadership: Define source-to-target reconciliation controls and data-quality rules across source systems, ETL pipelines, Snowflake or equivalent warehouse layers, semantic models, and reporting outputs. Automation Strategy and Architecture: Define scalable automation architecture using Python, PyTest, Playwright, API frameworks, reusable libraries, test-data utilities, reporting, and CI/CD integration. Prioritize automation based on risk, business value, repeatability, effort, and maintainability. Visual Regression and Baseline Governance: Establish Playwright or comparable browser automation for Power BI navigation, screenshot capture, visual comparison, threshold management, approved baseline updates, and defect triage for unexpected changes. Azure DevOps Traceability and Controls: Establish requirements-to-test-to-defect traceability, evidence standards, dashboards, workflow controls, defect templates, and execution reporting in Azure DevOps or an approved comparable tool. Defect Governance and Root Cause Analysis: Lead defect triage, severity and priority alignment, defect-ageing reviews, root-cause analysis, corrective-action tracking, fix verification, and learning from escaped defects. Release Readiness and Executive Reporting: Provide concise decision-oriented reporting on requirements and test coverage, execution progress, defect trends, open high-severity risks, automation coverage, dependencies, residual exposure, and release recommendations. Cross-Functional Quality Leadership: Partner with product owners, business analysts, engineering leaders, data engineers, Power BI developers, architects, scrum masters, security teams, and business stakeholders to make requirements testable and address quality risks early. Capability Building and Knowledge Sustainability: Mentor QA engineers, conduct reviews, define reusable templates and assets, maintain current operating procedures, facilitate knowledge transfer, improve backup readiness, and reduce dependency on individual subject-matter experts. Continuous Improvement and Innovation: Use quality metrics, defect patterns, delivery retrospectives, and value-stream analysis to identify process improvements. Evaluate AI-assisted testing, specification-driven approaches, and new tooling through governed proofs of concept. Compliance, Security and Test Data: Ensure QA practices follow approved privacy, masking, access-control, environment, open-source package, and secure test-data requirements. Stakeholder and Delivery Management: Manage competing QA priorities across products, communicate trade-offs and resource constraints, escalate blockers early, and align testing investment with customer and business outcomes. About you Qualifications: 10–15 years of software quality assurance experience, including technical leadership for complex data, analytics, reporting, API, and application testing. Demonstrated experience defining QA strategy, governance, standards, entry and exit criteria, evidence expectations, release gates, and sign-off recommendations. Advanced SQL and data-validation capability covering reconciliation, profiling, joins, aggregations, transformation-rule validation, and investigation of data discrepancies. Strong Power BI quality-assurance experience covering DAX, semantic models, data, interactions, role-based access, refresh, performance, visual standards, and regression governance. Strong data and ETL testing experience with Snowflake, Azure Synapse, SQL Server, SSMS, ADF, or comparable cloud data platforms and pipeline technologies. Hands-on Python and PyTest experience, including reusable framework design, fixtures, utilities, assertions, reporting, and automation maintainability. Experience