Vice President, Data Modernization - Data Readiness & Metadata Standards

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...