Senior Principal Architect, Data & Analytics Services

JPMorgan Chase JPMorgan Chase · Banking · Jersey City, NJ +1 · Corporate Sector

Senior Principal Architect role focused on driving AI adoption and establishing AI-enabled engineering patterns within financial services. The role involves owning target-state architectures for data platforms, leading modernization roadmaps, and ensuring compliance with data governance and resiliency standards. Key responsibilities include establishing reuse-first AI-enabled patterns, mentoring architects, and translating complex technical issues to leadership. The role requires expertise in data architecture, cloud platforms, and shaping/executing AI and Generative AI strategy, including LLM integration and responsible AI practices.

What you'd actually do

  1. 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
  2. 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
  3. Drives the modernization roadmap for critical data applications, balancing technical debt reduction, platform resilience, scalability, cost, and time-to-market
  4. Establishes reuse-first AI-enabled engineering patterns across SDLC and toolchain practices, ensuring traceability, auditability, and security controls are embedded by design
  5. 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

Skills

Required

  • 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

Nice to have

  • Experience in global financial services and regulated environments; familiarity with compliance regimes (OCC, PRA, EBA, MAS) and architecture support for regulatory requirements
  • 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, part

What the JD emphasized

  • AI adoption across a portfolio
  • AI-enabled engineering patterns
  • AI strategy within a technology organization
  • LLM integration patterns
  • MLOps
  • model governance
  • responsible AI practices
  • safe adoption of enterprise-authorized AI capabilities
  • AI-enabled architectural patterns
  • agentic workflows

Other signals

  • AI adoption across a portfolio
  • AI-enabled engineering patterns
  • AI strategy within a technology organization
  • LLM integration patterns
  • MLOps
  • model governance
  • responsible AI practices
  • safe adoption of enterprise-authorized AI capabilities
  • AI-enabled architectural patterns
  • agentic workflows