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)

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