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Data Insight use case: strong data management experience is needed.Business Execution Consultant - GenAI & Wholesale Data ManagementWells Fargo is seeking a Business Execution Consultant to support strategic Generative AI (GenAI), Data, and Digital Transformation initiatives within Commercial Banking and Corporate & Investment Banking (Wholesale). This role combines product management, business analysis, and data management expertise to drive delivery of AI-enabled solutions while ensuring alignment with enterprise data standards, governance requirements, and business objectives.The ideal candidate will have deep experience working within Wholesale Banking data environments, partnering across business, technology, data engineering, data management, analytics, risk, and operations teams to translate complex business processes and data requirements into scalable solutions.Key ResponsibilitiesProduct Management & Delivery

• Support product roadmap execution, backlog prioritization, and Agile delivery activities.
• Translate business needs into user stories, functional requirements, and acceptance criteria.
• Partner with technology, architecture, and data teams to deliver scalable business capabilities.
• Coordinate testing, UAT execution, release validation, and production readiness activities.
• Identify and manage dependencies, risks, and delivery challenges across multiple workstreams.
• Drive adoption of AI and data-driven capabilities that improve commercial banking processes and decision-making.

Wholesale Data Management & Business Analysis

• Elicit, document, and manage business, data, and functional requirements across Wholesale Banking domains.
• Lead data discovery, profiling, lineage analysis, and source-to-target mapping activities.
• Partner with Data Management organizations to define and maintain critical data elements, business glossaries, metadata, and data quality controls.
• Analyze complex wholesale banking datasets including client, borrower, counterparty, facility, collateral, relationship, and financial data.
• Define business rules, data quality requirements, controls, and reconciliation processes across upstream and downstream systems.

Data Analysis & Solution Enablement

• Perform data analysis to support product development, operational improvements, and GenAI use cases.
• Develop and execute SQL-based analysis to validate requirements, investigate issues, and support business decisions.
• Define data requirements for APIs, system integrations, analytical models, and AI solutions.
• Collaborate with data engineering and analytics teams to improve data accessibility, quality, and usability.
• Support implementation of GenAI solutions through data readiness assessments, prompt design support, testing, and validation activities.

Stakeholder & Cross-Functional Leadership

• Partner with business stakeholders, Product Managers, Data Management teams, Technology, Risk, Compliance, Analytics, and Data Science organizations.
• Facilitate working sessions to align business processes, data definitions, requirements, and priorities.
• Communicate project status, risks, dependencies, and key decisions to leadership and stakeholders.
• Create executive-ready documentation, process flows, requirements artifacts, and data management deliverables.

Required Qualifications

• 5+ years of experience in Business Analysis, Data Management, or related disciplines.
• Strong experience within Wholesale Banking, Commercial Banking, Corporate Banking, Treasury Management, Lending, Risk, or Financial Services.
• Demonstrated experience gathering and translating business and data requirements into actionable solutions.
• Hands-on experience with Wholesale Banking data domains, including customer, borrower, relationship, loan, facility, collateral, exposure, or financial data.
• Advanced SQL skills and strong analytical problem-solving capabilities.
• Experience with data lineage, source-to-target mapping, data quality validation, metadata management, and integration analysis.
• Experience working within Agile delivery environments and software development lifecycles.
• Strong stakeholder management and cross-functional collaboration skills.

Technical SkillsData & Analytics

• SQL (required)
• Teradata, Snowflake, MongoDB, or similar platforms
• Data profiling, data lineage, metadata management, and data quality frameworks
• Source-to-target mapping and requirements documentation
• Data governance and stewardship concepts

Integration & Technology

• APIs, JSON, Swagger/OpenAPI
• System integration and data flow analysis
• Postman
• JIRA and Confluence
• Figma or similar requirements/design tools

Delivery Frameworks

• Agile, Scrum, and SAFe methodologies
• Product backlog and requirement management
• UAT and release management

Preferred Qualifications

• Experience within Wells Fargo Wholesale Data Management (WDM), Enterprise Data Management, or Data Governance organizations.
• Knowledge of Wells Fargo data frameworks, data governance practices, and Data Management Policy requirements.
• Experience working with Critical Data Elements (CDEs), Data Lineage, Business Metadata, Data Quality Controls, and Data Stewardship models.
• Experience supporting Commercial Banking, Corporate & Investment Banking (CIB), Treasury Management, or Credit Risk platforms.
• Experience supporting AI, Machine Learning, or GenAI products.
• Familiarity with LLMs, prompt engineering, retrieval-augmented generation (RAG), and Responsible AI principles.
• Experience working within highly regulated financial services environments.

Ideal CandidateA highly analytical and execution-oriented professional with strong Wholesale Banking Data Management experience who can bridge business, product, data, and technology teams. This individual brings expertise in enterprise data governance, data quality, lineage, and wholesale banking data domains, while also possessing the product and delivery skills necessary to successfully execute strategic GenAI and digital transformation initiatives. EEO: Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.