Test Lead with Wealth Management & AI-driven testing experience

Role: Test Lead with Wealth Management & AI-driven testing experience

Location: Windsor, CT / Boston, MA / New York, NY

Mode of Hire: Full Time

Position Summary

We are seeking a highly experienced Test Lead - Wealth Management to establish and lead an enterprise-wide Quality Engineering and Testing Practice for large-scale Wealth Management transformation program. This role requires a strategic leader who can define end-to-end testing methodologies, implement AI-powered test automation frameworks, and drive collaboration across Business, Product, Engineering, Architecture, and Operations teams.

A strategic Quality Engineering leader who combines deep Wealth Management knowledge, strong program leadership, AI-driven testing expertise, and the ability to bridge Business and Engineering teams while building a modern, enterprise-scale testing practice capable of supporting large transformation programs.

Key Responsibilities

Quality Engineering & Test Practice Leadership

• Build and lead an enterprise-wide testing and quality engineering practice for Wealth Management initiatives.
• Define testing governance, operating models, standards, tools, KPIs, and best practices across multiple programs and portfolios.
• Develop a long-term quality engineering roadmap aligned with business and technology transformation objectives.
• Establish test management processes covering requirement validation, test planning, execution, defect management, reporting, and release readiness.

End-to-End & UAT Strategy

• Design and execute comprehensive End-to-End (E2E), System Integration Testing (SIT), and User Acceptance Testing (UAT) strategies.
• Define testing approaches across Wealth Management business capabilities including:
• Client onboarding
• Advisor platforms
• Portfolio management
• Asset allocation
• Financial planning
• Trading and execution
• Performance reporting
• Regulatory and compliance functions
• Ensure complete business process validation across integrated applications and vendor platforms.
• Establish business-led UAT frameworks and governance processes that improve stakeholder engagement and acceptance outcomes.

AI-Powered Test Automation Leadership

• Define and implement an AI-driven automation testing framework that supports functional, regression, integration, API, and data validation testing.
• Leverage Generative AI and intelligent automation tools for:
• Automated test case generation
• Requirement-to-test traceability
• Test data creation
• Defect prediction and analysis
• Automated script maintenance
• Test optimization and coverage analysis
• Create a shift-left testing strategy by integrating AI-based testing capabilities into CI/CD pipelines.
• Drive automation adoption across all testing phases to improve speed, coverage, and quality.

Program & Stakeholder Leadership

• Act as the primary quality leader for large-scale transformation programs.
• Facilitate strategic discussions between Business, Product Owners, Engineering, Program Management, and Executive stakeholders.
• Translate business requirements into enterprise testing strategies and measurable quality outcomes.
• Drive alignment between business expectations and technical implementation through structured test planning and validation activities.
• Lead quality governance forums, steering committee discussions, and executive quality reviews.

Test Delivery & Execution Management

• Manage multiple testing workstreams across geographically distributed teams.
• Oversee test planning, resource management, environment readiness, execution tracking, and defect triage activities.
• Implement risk-based testing models and quality gates across releases.
• Drive release readiness assessments and go-live recommendations.
• Monitor quality metrics and proactively identify program risks and mitigation plans.

Data, Integration & Non-Functional Testing

• Establish strategies for:
• Data validation and reconciliation
• API and integration testing
• Performance and scalability testing
• Security and compliance testing
• Production validation testing
• Ensure data integrity across Wealth Management ecosystems and downstream reporting platforms.

Team Building & Capability Development

• Build and mentor a high-performing Quality Engineering organization.
• Define role-based competencies, career paths, and skill development plans.
• Promote adoption of AI, automation, and modern testing practices across engineering teams.
• Foster a quality-first engineering culture across delivery organizations.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.
• 12+ years of experience in Quality Engineering, Software Testing, or Test Management.
• 5+ years leading enterprise-scale testing programs within Wealth Management, Asset Management, Investment Management, Brokerage, or Financial Services.
• Proven experience establishing testing centers of excellence or quality engineering practices.
• Extensive experience leading End-to-End, UAT, SIT, and Release Validation programs.
• Strong stakeholder management experience with senior business and technology leaders.
• Experience managing large distributed testing teams and vendor partners.

Wealth Management Domain Expertise

Strong understanding of:

• Advisory and Wealth Management platforms
• Investment products and portfolios
• Managed accounts
• Trading and investment operations
• Financial planning solutions
• Client servicing workflows
• Regulatory and compliance controls
• Performance measurement and reporting
• Market data and investment analytics

Technical Skills

• AI-enabled Test Automation Platforms
• Selenium, Playwright, Cypress, Tosca, or equivalent
• API Testing (Postman, Rest Assured)
• CI/CD Tools (Azure DevOps, GitHub Actions, Jenkins)
• Test Management Platforms (Jira, Xray, Zephyr, ALM)
• Cloud Platforms (Azure, AWS, Google Cloud Platform)
• SQL and Data Validation Frameworks
• Performance Testing Tools
• GenAI applications for Quality Engineering and Test Automation

Success Metrics

• Increased automated test coverage across enterprise applications
• Reduced regression testing cycle times through AI automation
• Improved defect detection in earlier phases of delivery
• Improved UAT success rates and business satisfaction
• Faster release cycles with reduced production incidents
• Establishment of scalable enterprise quality engineering operating model
• Enhanced collaboration between Business and Engineering organizations