Role: Test Lead with Wealth Management exp.
Location: New York, NY/Windsor, CT/Boston, MA (Hybrid 3 days onsite a week)
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.
· The ideal candidate will bring deep expertise in Wealth Management platforms and business processes, while creating a modern, scalable, AI-enabled testing ecosystem that accelerates delivery, improves quality, and reduces production risk across complex programs.
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:
o Client onboarding
o Advisor platforms
o Portfolio management
o Asset allocation
o Financial planning
o Trading and execution
o Performance reporting
o 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:
o Automated test case generation
o Requirement-to-test traceability
o Test data creation
o Defect prediction and analysis
o Automated script maintenance
o 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:
o Data validation and reconciliation
o API and integration testing
o Performance and scalability testing
o Security and compliance testing
o 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 Skills:
· 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.
Required 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
