Senior Principal Data Architect

As a Senior Principal Data Architect at JPMorganChase within the IAM - Data Services Organization, you provide specialized expertise to influence the target state architecture and strategy. Work across teams to implement and advance the adoption of complex, strategic, global data architecture solutions that will give JPMorganChase a competitive advantage.

This is your moment to make a difference and advance the strategic direction of technology on a global scale. Your unique perspective is the perfect fit for this forward-thinking team of architects.

• Advises and leads on data architecture strategy and development of multiple products, applications, and technologies across a portfolio
• Leads reuse-first adoption of enterprise-authorized AI capabilities within the work environment to accelerate data architecture analysis, portfolio-level option evaluation, and delivery of strategic global solutions, with human-in-the-loop validation and appropriate handling of sensitive data.
• 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 data architecture for the target state.
• You will help architect and/or build Trace, a platform that continuously monitors adherence to internal controls and produces traceable, auditable evidence to support assurance and readiness for internal/external audits.
• The role partners with IAM, control owners, and engineering teams to onboard control signals, model entities/relationships, and deliver repeatable evidence generation with strong resiliency and security.
• Drives firmwide adoption and implementation of data architecture specifications in technology solutions and establishes and leads a data architecture governance body
• Creates durable, reusable data frameworks using new technology to meet the needs of the business
• Establishes portfolio-level guardrails for AI-assisted and agentic workflows used in data architecture governance and delivery, including traceability/auditability and control expectations aligned to resiliency and security standards.
• Design the control evidence data model (entities, relationships, temporal/versioning, provenance/lineage).
• Define patterns for control signal ingestion, normalization, and continuous evaluation (batch/stream as applicable).
• Establish auditability/traceability standards: evidence immutability, lineage, approvals, and explainability.
• Drive integration patterns with upstream control sources and downstream reporting/attestation consumers.
  
   

Required qualifications, capabilities, and skills

• Practical experience delivering system design, application development, testing, and operational stability, including extensive practical cloud native experience
• Proven track record setting data modeling standards and influencing multiple teams/products.
• Deep expertise in graph data modeling (entity/relationship design, identity resolution patterns, temporal modeling, constraint design).
• Demonstrated experience leading safe adoption of enterprise-authorized AI capabilities within the work environment across data architecture workflows, including validation practices and awareness of data sensitivity.
• Ability to evaluate and govern AI-enabled data architecture patterns (including agentic workflows) with clear control boundaries, auditability, and human approval checkpoints aligned to resiliency and security expectations.
• Ability to evaluate current and emerging technologies to recommend the best data architecture solutions or approach to achieve the future state architecture

Preferred qualifications, capabilities, and skills

• Hands-on experience with graph technologies and query languages and/or ontology frameworks 
• Experience with metadata management, data governance, MDM, and lineage/provenance implementations.
• Familiarity with AI/ML enablement via graphs (feature generation, embeddings, relationship-aware retrieval), with strong grounding in data controls.