Senior Principal Architect, Data & Analytics Services

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.

If you are excited about shaping the future of technology and driving significant business impact in financial services, we are looking for people just like you. Join our team and help us develop game-changing, high-quality solutions.

As a Senior Principal Architect at JPMorganChase within Corporate Technology – Corporate Data & Analytics Services (CDAS), you are an integral part of a team that works to develop high-quality architecture solutions for various software applications and platform products. You drive significant business impact and help shape the target state architecture through your capabilities in multiple architecture domains.

You will own the target-state architecture for platforms spanning Reference Data, KYC, and Common Trade and Position Data. You’ll drive modernization, governance, and AI adoption across a portfolio supporting firmwide finance and risk processing. Your leadership will influence technology investment, foster architectural craftsmanship, and ensure compliance with data governance and resiliency standards.
 

Job Responsibilities

• Owns and maintains the target-state and transitional architectures for the CDAS tower, spanning Reference Data, KYC, and Common Trade and Position Data platforms that support Finance and Risk functions firmwide
• Leads architecture governance across the tower — establishing design principles, guardrails, and review processes that ensure solutions are built consistently and cohesively with CT architectural direction
• Drives the modernization roadmap for critical data applications, balancing technical debt reduction, platform resilience, scalability, cost, and time-to-market
• Chairs or contributes to Architecture Review Boards; represents the tower in firmwide and CT architecture forums, contributing reference patterns and governance guidance for broader adoption
• Acts as primary technical interface with senior leaders, CTOs, Chief Architects, and business stakeholders — building trusted advisor relationships and influencing technology investment decisions
• Provides hands-on oversight and coaching to domain architects and engineering leads; intervenes directly in complex design challenges and escalated technical decisions
• Ensures all platforms adhere to firmwide data governance, data risk management policy, regulatory obligations, and security and resiliency standards — including multi-region resiliency requirements
• Establishes reuse-first AI-enabled engineering patterns across SDLC and toolchain practices, ensuring traceability, auditability, and security controls are embedded by design
• Mentors and develops a high-performing architecture practice; fosters a community of craftsmanship across architects, engineers, and data practitioners in the tower
• Translates highly complex technical issues, trends, and approaches to leadership to drive the firm’s innovation and enable leaders to make strategic, well-informed decisions about target state architecture Sr Principal Architect.docx
• Establishes portfolio-level guardrails for AI-assisted and agentic workflows used in architecture and engineering governance, including traceability/auditability and control expectations aligned to resiliency and security standards 

  Required Qualifications, Capabilities, and Skills

• Formal training or certification on architecture and software engineering concepts and 10+ years of applied experience, including 7+ years leading architecture for large-scale, mission-critical data platforms in complex, regulated environments
• Deep expertise in data architecture patterns: data mesh, lakehouse, canonical data modeling, domain-driven design, event-driven and streaming architectures, CQRS, and API-led connectivity
• Proven track record leading architecture governance at portfolio scale — design principles, ARB participation, standards enforcement, and cross-team alignment
• Strong knowledge of AWS, cloud-native platforms , container orchestration, CI/CD, IaC, observability, and platform engineering practices
• Experience with real-time and batch data processing technologies (Kafka, Databricks, Spark, Snowflake or equivalent) and the governance and resiliency patterns that operate within them
• Hands-on expertise in data governance: metadata management, lineage, data quality, classification, retention, and localization controls
• Experience shaping and executing AI and Generative AI strategy within a technology organization — including LLM integration patterns, MLOps, model governance, and responsible AI practices
• Demonstrated experience leading safe adoption of enterprise-authorized AI capabilities within the work environment across architecture workflows, including validation practices and awareness of data sensitivity
• Ability to evaluate and govern AI-enabled architectural patterns (including agentic workflows) with clear control boundaries, auditability, and human approval checkpoints aligned to resiliency and security expectations
• Security and resiliency expertise: zero trust, multi-region DR, secrets management, chaos engineering, and site reliability practices
• Executive presence with the ability to simplify complexity, influence across C-suite and engineering audiences, and drive consensus across competing priorities

Preferred Qualifications, Capabilities, and Skills

•  Experience in global financial services and regulated environments; familiarity with compliance regimes (OCC, PRA, EBA, MAS) and architecture support for regulatory requirements
•  Relevant certifications: AWS Solutions Architect Professional, TOGAF, CISSP, or equivalent
•  Hands-on pragmatism — able to review code-level artifacts, data models, and infrastructure designs while maintaining a strategic lens
•  Experience with enterprise-scale data platform modernization, including legacy migration and strangler-fig approaches
•  Familiarity with FinOps and cost-aware data design (storage tiers, partitioning, lifecycle policies)
•  Experience with firmwide data risk management frameworks and evidence-based audit support