Lead PEGA Architect

• Serve as the Product Architect for platforms supporting Payment Operations within the COO lines of business
• Act as the primary technology expert working closely with both business and technology executives to develop technology strategy and architecture
• Provide ongoing architectural support and direction for multiple technical efforts across several agile squads
• Provide strategic direction in the development and maintenance of architectures for Payment Operations that are transformative and in compliance with the Enterprise Architecture
• Create product architecture artifacts as required, including current, transition, and target state architecture documentation
• Communicate the target architectures and direction to both management and systems-related teams within Payment Operations and direct implementation of the architecture
• Act as a transformational agent for change across TCOO
• Drive the development and alignment of product strategy and roadmap with business objectives

• 5+ years of Systems Architecture experience, or equivalent demonstrated through work experience, training, military experience, education
• 3+ years of experience with PEGA case management/business process management system
• 5+ years leading medium to large-scale architecture programs
• Strong communication and stakeholder influence skills, including at senior levels
• Deep understanding of architecture best practices and domain-driven microservices
• Proven ability to lead through ambiguity and change in complex environments
• Proficiency in architecture modeling tools (e.g., Bizzdesign, ArchiMate)
• Demonstrated experience integrating AI/ML solutions into enterprise architectures
• Strong grasp of AI governance, lifecycle management, and ethical deployment
• Familiarity with data engineering practices supporting AI (e.g., pipelines, feature engineering)
• Ability to assess platform health and readiness through tech portfolio evaluations

• Demonstrated experience integrating AI/ML solutions into enterprise architectures
• Strong grasp of AI governance, lifecycle management, and ethical deployment
• Familiarity with data engineering practices supporting AI (e.g., pipelines, feature engineering)
• Ability to assess platform health and readiness through tech portfolio evaluations

Hybrid work schedule