SQA Manager

Arthur J. Gallagher · Bengaluru

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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.

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. 

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.

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 with Playwright or a comparable browser automation framework for UI and visual regression testing and baseline governance.
• Experience with API testing, CI/CD integration, source control, automated execution reporting, and controlled adoption of open-source packages.
• Advanced Azure DevOps experience for test management, requirements traceability, defect governance, dashboards, sprint visibility, and quality reporting.
• Experience leading defect triage, root-cause analysis, risk assessment, release-readiness reviews, and executive or leadership-level quality reporting.
• Ability to mentor QA engineers, influence without formal authority, lead cross-functional quality discussions, and create sustainable knowledge-transfer practices.
• Strong business acumen, strategic communication, stakeholder management, prioritization, analytical thinking, and evidence-based decision-making.

Preferred Qualifications:

• Experience establishing or scaling a QA Center of Excellence or shared quality-engineering capability.
• Experience with AI-assisted testing, specification-driven test generation, automated defect creation, or governed use of enterprise AI platforms.
• Performance testing experience using JMeter, k6, or comparable tools; accessibility testing knowledge is an advantage.
• Knowledge of data lineage, metadata, observability, data contracts, masking, access controls, and secure non-production environments.
• Experience measuring automation return, manual-effort reduction, defect leakage, coverage, cycle time, and release confidence.
• ISTQB Advanced/Test Manager, CSQE, Microsoft Power BI, Snowflake, Azure, Python, Agile/Scrum, Lean Six Sigma, or related certification or training.

Education And Experience:

• Bachelor’s degree required; a degree in computer science, information technology, engineering, data, or a related technical discipline is preferred.
• 10–15 years of relevant QA experience. Talent Acquisition and Compensation should confirm the final career level, career band, and education wording before publication.

At Gallagher, we believe supporting our colleagues goes far beyond the role itself. For more information, visit our Benefits page.

• Competitive compensation
• Comprehensive benefits programs designed to support your well-being 
• Career development opportunities and ongoing learning 
• A collaborative, people-first culture with accessible leadership 
• The opportunity to do meaningful work with global reach and local impact 

At Gallagher, we are dedicated to building an inclusive and authentic workplace. If your past experience doesn’t align perfectly, we encourage you to join our Talent Community to stay connected to additional career opportunities. At times, we will consider transferable skills from previous roles.

Gallagher is an affirmative action/equal opportunity employer (Minorities/Females/Veterans/Disabled)

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.

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 with Playwright or a comparable browser automation framework for UI and visual regression testing and baseline governance.
• Experience with API testing, CI/CD integration, source control, automated execution reporting, and controlled adoption of open-source packages.
• Advanced Azure DevOps experience for test management, requirements traceability, defect governance, dashboards, sprint visibility, and quality reporting.
• Experience leading defect triage, root-cause analysis, risk assessment, release-readiness reviews, and executive or leadership-level quality reporting.
• Ability to mentor QA engineers, influence without formal authority, lead cross-functional quality discussions, and create sustainable knowledge-transfer practices.
• Strong business acumen, strategic communication, stakeholder management, prioritization, analytical thinking, and evidence-based decision-making.

Preferred Qualifications:

• Experience establishing or scaling a QA Center of Excellence or shared quality-engineering capability.
• Experience with AI-assisted testing, specification-driven test generation, automated defect creation, or governed use of enterprise AI platforms.
• Performance testing experience using JMeter, k6, or comparable tools; accessibility testing knowledge is an advantage.
• Knowledge of data lineage, metadata, observability, data contracts, masking, access controls, and secure non-production environments.
• Experience measuring automation return, manual-effort reduction, defect leakage, coverage, cycle time, and release confidence.
• ISTQB Advanced/Test Manager, CSQE, Microsoft Power BI, Snowflake, Azure, Python, Agile/Scrum, Lean Six Sigma, or related certification or training.

Education And Experience:

• Bachelor’s degree required; a degree in computer science, information technology, engineering, data, or a related technical discipline is preferred.
• 10–15 years of relevant QA experience. Talent Acquisition and Compensation should confirm the final career level, career band, and education wording before publication.

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