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 Bachelor’s degree in a quantitative, scientific, or technical field (for example, Mathematics, Statistics, Computer Science, Engineering, or Economics), or equivalent practical experience. Seven years of relevant experience in data science, data management, data governance, data quality, or analytics engineering, including setting standards and influencing across teams. Deep knowledge of metadata management and data catalog tools, with emphasis on discoverability, lineage, and interpretability. Hands-on experience with structured and unstructured data at scale, including profiling, cleansing, standardizing, and documenting large datasets on enterprise platforms or data products. Strong command of data quality frameworks and the ability to diagnose, measure, and drive remediation of quality issues. Understanding of ontology and semantic/context layers, and how consistent definitions improve reuse across analytics and artificial intelligence systems. Solid Structured Query Language (SQL) skills and analytical problem-solving, including root-cause investigation across large data volumes. A first-principles mindset that questions assumptions and ensures data makes sense in context, not just in aggregate. Working knowledge of how conversational analytics, natural-language querying, and agentic AI consume data, and the data conditions they depend on. Strong attention to detail and an uncompromising commitment to accuracy. Proven experience collaborating across product, engineering, and business teams in a regulated environment, with clear written and verbal communication for senior stakeholders. Preferred qualifications, capabilities, and skills Experience in consumer banking or another large-scale, high-volume data environment. Exposure to building or governing semantic models and metrics layers for enterprise analytics. Familiarity with data domain modeling and standardization across multiple business units. Scripting or full-stack skills (for example, Python) that support data profiling, enrichment, and rapid prototyping. Applied exposure to artificial intelligence, machine learning, and generative AI concepts from the perspective of a consumer of well-governed data.