Data Product Management Consultant - Wells Fargo Business

Wells Fargo•Minneapolis, MN

20h•$87,000 - $154,000

About The Position

Wells Fargo is seeking a Data Product Management Consultant to join the Consumer Data Products (CDP) team supporting Consumer lines of business. CDP is rationalizing and modernizing consumer data across domains such as Branch, Deposits, and Small Business; building next-generation analytic data repositories in the cloud; and developing strategic data capabilities that simplify the analyst and end-user experience. The team also supports adoption of these capabilities and retirement of legacy platforms. The role will serve as an Analyst for the WF Business data product team, initially focused on modernizing business banker field reporting and establishing a trusted, strategic data foundation for banker and relationship manager insights. The work will expand into AI and generative AI enablement, including data and business requirements for banker productivity capabilities such as prioritized outreach, relationship overviews, recommended actions, and other insight-driven experiences. The consultant will support data discovery, requirements and user-story development, user acceptance testing, reporting modernization, data quality and governance, validation and reconciliation, and collaboration with AI, analytics, business, and technology partners. The role also involves documenting data needs, metadata, policies, standards, processes, issue resolution, and remediation updates.

Requirements

2+ years of data product or data management experience, or equivalent demonstrated through work experience, training, military experience, or education.

Nice To Haves

Knowledge of modern data and analytics ecosystems, with experience in technologies such as Google BigQuery, Teradata, Hadoop, Spark, Power BI, Tableau, Alteryx, or comparable cloud, data, and business intelligence platforms.
Proficiency in SQL.
Experience defining requirements for enterprise reporting modernization, including source-to-target mapping, metric definitions, data lineage, data quality controls, and reconciliation.
Working knowledge of AI and generative AI concepts, including how trusted, governed data supports grounded and explainable AI-enabled experiences; direct AI delivery experience is preferred but not required.
Experience working in an agile scrum framework, including backlog refinement, user stories, acceptance criteria, sprint planning, demonstrations, and user acceptance testing.
Proficiency with all Microsoft Office applications and ability to construct PowerPoint presentations for executive communications.
Experience working with data in the financial services industry.
High degree of intellectual curiosity and desire to learn and discover.
Excellent writing skills.

Responsibilities

Support strategic data and reporting capabilities within the CDP portfolio, including data discovery, user story and requirements writing, and hands-on user acceptance testing.
Support cross-functional initiatives to modernize WF Business field reporting and consolidate fragmented reporting data onto strategic platforms.
Analyze and help manage complex datasets, focusing on business definitions, data lineage, quality, reconciliation, controls, reporting and AI consumer needs, and data governance standards.
Develop prototype designs for data capabilities that improve data availability for field reporting, performance and portfolio insights, analytics, and responsible AI enablement.
Define and execute data validation and reconciliation testing to confirm reporting accuracy, completeness, stability, and alignment to approved business rules before release.
Partner with AI, analytics, and technology teams to translate business requirements into executable data requirements.
Identify data quality metrics and execute data quality audits to benchmark data quality.
Document data governance, data quality, and metadata policies, standards, tools, processes, or procedures for enterprise data management.
Support communications with documentation related to requirements, design decisions, issue closure, or remediation updates.
Gather data requirements and document data needs throughout the data product life cycle.
Communicate low- to moderate-complexity data problems and initiatives in a digestible manner to all audiences.
Support root cause analysis through data profiling, data or business analysis, and data mapping.
Work with business and technology partners or subject matter professionals to document or maintain business or technical metadata about systems, business elements, or data elements.