Wealth Management - Senior Lead Data Architect - Vice President

Join a team that develops high-quality data architecture solutions to deliver governed, high-quality, AI-ready data for business-critical applications and use cases. Help shape the global target-state architecture through capabilities across multiple data architecture domains.

Job Summary

As a Lead Data Architect at JPMorgan Chase within the Private Bank Chief Data & Analytics Office, you are an integral part of the team that develops new data-product offerings and leads end-to-end data-product design. Leverage AI knowledge to create metadata artifacts, develop AI skills to automate tasks, and deliver Data Architecture as a Product.

Lead conceptual, logical, and physical data modeling within and across sub-domains. Create and maintain metadata artifacts such as business terms, glossaries, ontologies, taxonomies, and semantic/context layers for AI. Use modeling tools, such as Erwin, and AI-assisted techniques to accelerate metadata production while preserving governance rigor.

Job responsibilities

- Engage technical teams and business stakeholders to discuss and propose data architecture approaches to meet current and future needs
- Define the data architecture target state of the product and lead achievement of the strategy
- Participate in data architecture governance bodies
- Execute creative data architecture solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Own domain modeling and data design, including CDM/LDM/PDM for WM domains; model key entities with clear keys, relationships, and lifecycle states
- Define the semantic/context layer, including canonical business concepts and relationships; establish conformed dimensions and standardized measures (e.g., AUM, net flows, time-weighted return) with unambiguous definitions
- Produce and maintain metadata artifacts, including business glossaries (terms, definitions, stewardship), ontologies/taxonomies (concept hierarchy, relationships), context layer mappings (technical fields → business concepts → metrics), naming standards, and modeling conventions. Incorporate metadata into the data mesh delivery process, aligned with governance policy expectations
- Apply AI to accelerate metadata production and maintain metadata at scale (e.g., suggested term mappings, documentation generation, relationship inference), with human review gates and version control
- Design for AI readiness to ensure models support analytics and AI use through stable identifiers, entity resolution approaches, history (SCD/event modeling), feature-friendly structures, and clear semantics for retrieval and grounding
- Define standards for interoperability and evolution, including patterns for schema evolution, versioning, deprecation, and backward compatibility; ensure interoperability across platforms (warehouse/lakehouse/APIs/BI)

Required qualifications, capabilities, and skills

- Demonstrate 5 years of experience or equivalent expertise in data design, including data products, data lakes or data warehouses, and data modeling
- Apply advanced knowledge of the data-product development life cycle, design, and data analytics within a domain-driven, data mesh paradigm
- Demonstrate in-depth knowledge of the financial services industry and data ecosystem
- Demonstrate data modeling expertise and the ability to move fluidly from concept to logical to physical implementation
- Apply practical cloud-native experience
- Use AI tools to define requirements, shape design, and harvest and create metadata

Preferred qualifications, capabilities, and skills

- Demonstrated prior experience working in a highly matrixed, complex organization
- Wealth management domain expertise, including knowledge of IBOR, ABOR, investments and securities, alternatives, derivatives, fixed income, and equities
- Hands-on data profiling and analytics fluency with strong SQL skills
- Graph database design knowledge
- Risk/privacy awareness, including entitlements, data minimization, and classification, with the ability to partner effectively with controls teams