Vice President, Data Modernization - Data Readiness & Metadata Standards
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Job description
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., * 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.
Requirements
- 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.
Benefits & conditions
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.