Data is at the heart of how JPMorganChase drives innovation and competitive advantage. Join a team that turns complex, high-volume data into trusted assets that leaders can confidently use to make decisions and build new customer experiences. You will influence how data is defined, described, and governed-so it is easier to discover, interpret, and apply across analytics and artificial intelligence. This role offers meaningful visibility, cross-functional partnership, and the opportunity to shape firmwide standards through tangible delivery and rapid prototyping. Job summary As a Vice President in the Data Modernization program within Consumer & Community Banking Data & Analytics, you will lead work that makes structured and unstructured data more discoverable, interpretable, and dependable. You will define practical metadata and data domain patterns-business, technical, and operational-that help teams find, understand, and trust data at scale. You will partner with data owners and engineers to identify quality and definition gaps, prioritize fixes, and convert one-off improvements into scalable standards. You will translate technical progress into clear narratives and measurable outcomes that support roadmap decisions and executive updates. You will operate as a hands-on standards leader: comfortable in detailed data conversations, credible with engineers, and effective with senior stakeholders. You will balance governance and speed-setting clear expectations while enabling teams to move faster through reusable patterns, scorecards, and prototypes. You will help create the conditions for high-quality analytics, conversational querying, and generative AI experiences by improving the "readiness" of data upstream. Job responsibilities

• Shape and drive adoption of the enterprise data readiness framework across Consumer & Community Banking business units.
• Define and champion standards for business, technical, and operational metadata so data is well-defined, discoverable, and trustworthy at scale.
• Establish semantic and context standards that improve the consistency, interpretability, and reuse of data across analytics and artificial intelligence systems.
• Lead profiling of priority domains to surface definitional, lineage, and data-quality gaps, and partner with data owners to close them.
• Convert one-off fixes into repeatable, scalable enrichment patterns and mentor others to apply them.
• Advise data leaders and engineers on the quality and usability improvements that create the most value across large datasets.
• Build and showcase prototypes that demonstrate improved data readiness for analytics and AI-assisted use cases, including conversational and agentic experiences.
• Own readiness scorecards and key performance indicators, translating progress into inputs for maturity assessments, roadmap decisions, and executive updates. Required qualifications, capabilities, and skills
• Bachelo.

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