Full Job Description
JOB DESCRIPTION
Join a centralized data strategy team building governed data foundations for one of JPMC’s largest service organizations, spanning hundreds of products with distinct systems, identifiers, lifecycle stages, and business rules. The team works upstream of analytics and operational execution to establish trusted product and client state, connect authoritative sources, and create reusable data patterns that support end-to-end processing, analytical insight, and responsible AI adoption.
As a Vice President on the Commercial & Investment Bank’s Global Services Data Analytics team, you will lead complex data initiatives from discovery through implementation and adoption. You will translate ambiguous business needs into clear data, integration, validation, and governance requirements; evaluate solution options within established strategy and architecture guardrails; and coordinate across Business, Product, Technology, and data-governance partners. Your work will help ensure that trusted data supports controlled execution, validated outcomes, reliable operational decision-making, and scalable analytical and AI-enabled capabilities.
Job Responsibilities
• Lead discovery for complex data and end-to-end initiatives, distinguishing the intended business outcome from the requested report, dataset, workflow, or tool.
• Investigate product definitions, systems of record, identifiers, business rules, interfaces, manual steps, and downstream dependencies to establish current state and identify processing gaps.
• Evaluate options within established strategy and architecture guardrails, determining what should be reused, connected, integrated, automated, deferred, or left unbuilt, and where human judgment is required.
• Translate needs into implementation-ready requirements covering data structures, sources, identifiers, interfaces, transformation logic, controls, ownership, validation criteria, and open decisions.
• Partner with Business, Product, Technology, Data Owners, and Data Stewards to connect trusted data to controlled execution, validate resulting state, and make approved outcomes available for downstream use.
• Formalize entities, identifiers, lifecycle stages, metrics, source relationships, assumptions, and limitations using documentation, schemas, analytical logic, operational evidence, and SME input.
