Senior Lead Data Management Analyst, Data & AI Enablement

Wells Fargo•Charlotte, NC

1d•Hybrid

About The Position

Wells Fargo is seeking a Senior Lead Data Management Analyst to lead strategic data and AI enablement initiatives within Wealth and Investment Management (WIM). In this highly visible individual contributor role, you will help transform business priorities and artificial intelligence (AI) use cases into governed, scalable data solutions that support analytics, automation, intelligent decision-making, and business growth. Reporting to the WIM Strategic Data & AI Enablement Director, you will serve as a strategic bridge between business stakeholders, data and technology organizations, helping define how enterprise data, metadata, semantic models, and AI-ready capabilities are designed, governed, and adopted. You will partner with business leaders, data product teams, architects, engineers, and governance organizations to shape data strategy, improve data accessibility, and enable trusted AI solutions across the enterprise. This role focuses on data strategy, business analysis, data architecture direction, governance, semantic enablement, and adoption rather than hands-on application development or engineering.

Requirements

7+ years of Data Management, Business Analysis, Analytics, or Project Management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of hands-on experience translating business needs into data requirements, source decisions, mappings, analytical findings, or governed data solutions

Nice To Haves

Experience in financial services, preferably wealth management, brokerage, trust, fiduciary services, investment management, or regulatory reporting.
Experience supporting enterprise data, analytics, business intelligence, digital transformation, or AI initiatives within complex organizations.
Experience leading data strategy, metadata management, data governance, semantic enablement, enterprise architecture, or data product initiatives.
Experience with ontology management, knowledge graphs, semantic technologies, or industry standards such as RDF, OWL, SHACL, SPARQL, JSON-LD, Protégé, or FIBO.
Knowledge of enterprise metadata, lineage, data quality, privacy, security, and governance frameworks.
Experience profiling data, evaluating fit-for-purpose data sources, and recommending scalable data provisioning solutions.
Understanding of data architecture, data modeling, APIs, semantic layers, virtualization, business intelligence platforms, and governed data products.
Experience working with modern cloud-based data platforms, enterprise data catalogs, metadata solutions, or governance technologies.
Experience applying Generative AI, Retrieval Augmented Generation (RAG), agentic AI, or related technologies to improve business processes, data discovery, governance, or productivity.
Ability to influence senior leaders, drive alignment across organizations, and navigate ambiguity in complex environments.
Experience mentoring teams in data governance, metadata management, semantic modeling, ontology development, or knowledge graph practices.
Strong communication skills with the ability to translate complex technical concepts into actionable business insights.

Responsibilities

Translate business priorities and AI use cases into scalable data products, trusted data assets, semantic models, APIs, and reusable enterprise capabilities.
Identify opportunities to improve data quality, metadata, lineage, user experience, interoperability, and adoption across existing data products.
Define business, data, governance, quality, and control requirements that support successful data-product design and delivery.
Partner with Data Product Designers and delivery teams to prioritize enhancements that increase business value, usability, and reuse.
Promote enterprise data strategy and support the adoption of governed data solutions across stakeholder groups.
Translate business requirements into data requirements, mappings, transformations, business rules, and consumption specifications.
Analyze source systems, data flows, metadata, and data quality through stakeholder engagement, research, profiling, and hands-on SQL analysis.
Evaluate authoritative data sources based on completeness, quality, timeliness, accessibility, lineage, governance controls, and business value.
Recommend data integration, data provisioning, and data architecture approaches that simplify and strengthen the enterprise data ecosystem.
Use profiling results, trends, exceptions, and findings to identify risks, improve requirements, and support decision-making.
Assess organizational readiness for AI initiatives, including metadata maturity, data quality, provenance, governance controls, privacy, access management, and monitoring capabilities.
Define data, semantic, retrieval, grounding, traceability, and governance requirements that support responsible AI solutions and intelligent agents.
Design and guide semantic frameworks including ontologies, knowledge representations, metadata standards, and business vocabularies.
Identify AI data risks, limitations, assumptions, and governance requirements and facilitate appropriate stakeholder engagement.
Leverage AI evaluation results to improve metadata quality, semantic design, source selection, and overall data readiness.
Identify and escalate data management, governance, semantic modeling, and AI-related risks.
Establish end-to-end lineage and traceability from source systems through data products, APIs, semantic layers, and business use cases.
Embed data ownership, privacy, security, permissible use, access management, quality monitoring, and change controls into solution recommendations.
Partner with risk, compliance, privacy, information security, governance, and architecture teams to support policy alignment and regulatory expectations.
Promote trusted, transparent, and well-governed data practices across the organization.