Technology Director – Data Management Tools & Governance Technology

Wells Fargo•Irving, TX

22h•Hybrid

About The Position

Wells Fargo is back in the office collaborating for fabulous outcomes! This role is a hybrid role and in the office three days a week. The role is seated in the offices where the cities are posted on the req. There is no Visa transfers or Visa sponsorships for this role. The Tech Director of Data Management Tools & Governance Technology will lead the modernization and unification of the enterprise’s data governance tooling ecosystem while managing high‑performing, globally distributed engineering and technology teams. You’ll set the technology strategy, drive execution, and elevate engineering excellence to ensure data is trusted, governed, and scalable for a top‑tier financial institution. This is a high‑impact leadership role for a builder—someone who simplifies complexity and develops strong data engineering organizations. We’re looking for a leader who has overseen large teams, empowered data engineering talent, and consistently delivered at scale. You’ve led global teams, built high‑performance cultures, and know how to get key programs over the finish line. Enterprise Data Governance Technology Leadership Define and deliver enterprise-wide data governance technology strategies Build capabilities to maintain policies, procedures, and frameworks for end‑to‑end data flow governance Technology Enablement & Tools Management Lead the execution and enhancement of automated data management tools and frameworks Drive adoption of modern productivity and analytics platforms (e.g., Microsoft Copilot, GitHub) Standards & Compliance Ensure alignment with regulatory, risk, and internal compliance requirements Monitor and govern data risks, including data quality and adherence to Data Management Policy standards Innovation & Modernization Promote modern engineering practices such as data virtualization, AI, automation, and advanced analytics Simplify data environments and raise data quality across domains Champion adoption of enterprise tooling and modernization frameworks Stakeholder Engagement & Oversight Partner with business, technology, and risk leaders to ensure alignment Engage with and evaluate data management vendors and campion best in class solutions Provide independent oversight and monitoring of data management risks and controls

Requirements

8+ years of Technology Strategic Leadership experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
4+ years of management or leadership experience
Proven ability to lead enterprise data strategy and governance programs
Expertise in data management tools, automation frameworks, and cloud-based platforms
Strong understanding of regulatory compliance and risk management principles
Excellent communication and stakeholder management skills
Experience building a strong culture of collaboration and achieving high standards
Must have a “Product” mindset and comprehensive understanding of Agile development and best practices
Experience with Metadata, lineage, and governance platforms such as Collibra, Informatica EDC/Axon, Alation, Solidatus
Experience with Cloud and data ecosystems such as Azure, AWS, GCP, Snowflake, Databricks, Microsoft Purview
Experience with Data quality and discovery tools such as Informatica IDQ, Ataccama, BigID, MANTA
Experience with AI & productivity tooling such as Azure OpenAI, Microsoft Copilot, GitHub Enterprise / Copilot
Experience with Master & reference data platforms such as Informatica MDM, Reltio, Semarchy
Experience with Data virtualization & integration technologies such as Denodo, MuleSoft, Fivetran

Nice To Haves

Experience with AI-driven data solutions and generative AI applications
Familiarity with master data management (MDM) and reference data frameworks
Advanced knowledge of data architecture and integration technologies
Technology experience with building and operating custom and commercial off‑the-shelf applications

Responsibilities

Define and deliver enterprise-wide data governance technology strategies
Build capabilities to maintain policies, procedures, and frameworks for end‑to‑end data flow governance
Lead the execution and enhancement of automated data management tools and frameworks
Drive adoption of modern productivity and analytics platforms (e.g., Microsoft Copilot, GitHub)
Ensure alignment with regulatory, risk, and internal compliance requirements
Monitor and govern data risks, including data quality and adherence to Data Management Policy standards
Promote modern engineering practices such as data virtualization, AI, automation, and advanced analytics
Simplify data environments and raise data quality across domains
Champion adoption of enterprise tooling and modernization frameworks
Partner with business, technology, and risk leaders to ensure alignment
Engage with and evaluate data management vendors and campion best in class solutions
Provide independent oversight and monitoring of data management risks and controls
Oversee the planning, development, and deployment of data management, governance and tooling solutions, ensuring each release meets non-functional requirements, security, and risk standards, and supports application modernization and cloud readiness.
Shape data governance and tooling technology and business requirements and product priorities, champion innovative and intelligent solutions, and promote, track and manage product solutions.
Engage with architects and senior engineers to assimilate Wells Fargo Technology technical strategies, next-generation capabilities and domain architecture. Support seamless migration paths and advance data strategy initiatives.
Maintain expertise in data governance and tooling, and related emerging technologies. Encourage adoption of pioneering tools and methodologies, including retrieval-augmented generation (RAG) and agentic workflows in generative AI applications.
Identify and resolve technology barriers, managing the overall software development cycle, build and deployment processes, and security standards, including Enterprise Gen AI processes (Model Risk Management, Data Use, Joint Architecture and Design Reviews, etc.)
Implement best practices for data governance and tooling, including deep customer-centricity in product development. Lead product development, deployment, monitoring, and maintenance, and track impact on organizational objectives.