Senior Director of Architecture - Infrastructure Data - Executive Director

If you are ready to lead the way in technology innovation at JPMorganChase, we’ve got an exciting opportunity for a technology leader like you to make a real difference in the industry.

Role Summary

As a Senior Director at JPMorgan Chase within Infrastructure Data Architecture, you'll be accountable for defining and driving the target data architecture for the Infrastructure Platform (IP) Reference & analytical Data, define Data domains, ensuring trusted, scalable, secure, and well-governed data products that enable analytics, reporting, and downstream consumption. This role sets architectural direction, influences senior stakeholders, and partners across product line engineering, product, governance, security, and risk to deliver measurable outcomes.

Job Responsibilities

• Leads multiple architecture and process implementations across departments to achieve firmwide architecture objectives and manages multiple stakeholders, complex projects, and large cross-product collaborations

• Owns and evolves the IP Data target-state architecture (conceptual/logical/physical), including domain boundaries, data contracts, canonical models, and integration patterns

• Sets reuse-first expectations for enterprise-authorized AI adoption within the work environment across architecture domains to accelerate delivery and improve decision quality, with human-in-the-loop validation and appropriate handling of sensitive data.

• Directly manages multiple areas with strategic transactional focus and provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business

• Acts as the primary interface with senior leaders, stakeholders, and technology leaders across IP, CTO & CTC to align priorities, communicate architecture strategy, and drive adoption of standards and reusable patterns

• Establishes cross-department governance for AI-assisted and agentic workflows used in architecture decisioning and delivery, including traceability/auditability and control expectations aligned to resiliency and security standards.

• Partners with IP product and engineering to translate business capabilities into data products, APIs, and datasets with clear SLAs and lifecycle management

• Defines and implements modeling standards (e.g., dimensional, event/stream models as needed), naming conventions, metadata requirements, and reference/master data approaches

• Partners with IP product and engineering to translate business capabilities into data products, APIs, and datasets with clear SLAs and lifecycle management

• Provides hands-on guidance to squads (patterns, templates, best practices), unblock complex initiatives, and ensure deliverables meet architectural intent

• Mentors architects and senior engineers; build domain architecture capability through coaching, communities of practice, and hiring input as needed

Required qualifications, capabilities, and skills

• Formal training or certification on architecture concepts and advanced applied experience

• Significant experience in Infrastructure data architecture roles within complex, regulated environments, with demonstrated ownership of domain-wide data strategy and execution

• Expertise in Data modeling (conceptual/logical/physical), schema evolution, and data contract design

• Proven Data governance experience, metadata/lineage, and data quality frameworks

• Expertise in Batch and streaming patterns; integration/API patterns for data access

• Significant experience of modern lakehouse/warehouse concepts and domain-oriented data product thinking

• Proven ability to influence at senior levels, resolve cross-team dependencies, and drive architectural adoption.

• Experience defining measurable outcomes (SLOs/KPIs), operating models, and standards that scale across multiple squads.

• Demonstrated experience leading safe adoption of enterprise-authorized AI capabilities within the work environment across multiple architecture teams, including validation practices and awareness of data sensitivity.

• Ability to define governance expectations for AI-assisted and agentic workflows, including human approval checkpoints, auditability, and control boundaries aligned to resiliency, security, and operational risk outcomes.

Preferred qualifications, capabilities, and skills

• Experience modernizing legacy data estates (migration, coexistence, decommission strategies).

• Familiarity with privacy, data residency, records retention, and risk/control requirements relevant to financial services.

• Experience with reference/master data management and entity resolution in complex domains.