Lead Data Risk Fusion Officer | Technology Risk Management

Wells Fargo•Charlotte, NC

4d•Hybrid

About The Position

Wells Fargo is seeking a Lead Technology Risk Officer to serve as a second line risk professional responsible for providing independent oversight of enterprise data risk. This role will advance how data risk is identified, monitored, and assessed across the enterprise through combining deep data risk management expertise with advanced analytical capabilities, including knowledge graph technologies, automated and AI-enabled monitoring, and risk signal integration. The Lead Officer will develop and apply innovative approaches to risk identification by leveraging graph-based analytics, automation, and artificial intelligence to identify emerging risk patterns, interconnected risk exposures, and potential failure scenarios across complex data ecosystems. The role requires expertise in data management, analytics, and emerging technologies, along with the ability to translate complex technical findings into actionable risk insights for business leaders, executive leadership, and governance forums.

Requirements

5+ years of Technology Risk experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.

Nice To Haves

Experience supporting second line risk management, audit, compliance, or governance functions.
Strong knowledge of data management principles including data quality, lineage, metadata, governance, controls, data sourcing, and data requirements.
Experience leveraging knowledge graph technologies, graph analytics, or semantic technologies to identify relationships, dependencies, and emerging risk patterns across large and complex data sets.
Experience with data analytics platforms, SQL, Python, graph query languages (Cypher, SPARQL, Gremlin, or equivalent), data visualization tools, workflow automation solutions, and AI-enabled analytical platforms.
Experience implementing AI-enabled monitoring, intelligent automation, workflow automation, anomaly detection, or continuous monitoring capabilities.
Strong communication and stakeholder management skills with the ability to translate complex technical concepts into actionable business insights and influence decision-making across business, technology, and risk functions.

Responsibilities

Perform second line oversight across assigned data risk exposures, maintaining a current understanding of data governance, metadata, lineage, and data quality practices, along with associated risk indicators.
Lead the development and execution of advanced data risk monitoring, analytics, and oversight that identify emerging risks, risk concentrations, and shifts in enterprise data risk exposures.
Lead the design and implementation of AI-enabled and automated monitoring initiatives that improve risk detection, monitoring coverage, scalability, and the effectiveness of second line oversight.
Leverage knowledge graph technologies to model, query, analyze, and visualize relationships across data, systems, controls, business processes, and risk events to identify hidden dependencies, interconnected risks, and potential business impacts.
Integrate multiple risk indicators including data quality metrics, lineage breaks, control performance, incidents, issues, policy exceptions, metadata changes, etc. into a consolidated data risk perspective.
Perform scenario analysis and assessments of emerging data-related risks, including risks associated with AI, advanced analytics, and evolving data ecosystems.
Identify interconnected risks across data, technology, and information security domains, providing insights into potential business impacts and systemic risk conditions.
Deliver clear and actionable risk insights through analytical reporting, dashboards, visualizations, and executive-level communications that support risk-informed decision making.
Communicate complex analytical findings and emerging risk insights to business, technology, data management, and risk leaders in a clear and actionable manner.
Provide technical guidance to Data Risk Fusion Officers and analysts, promoting consistent analytical methodologies, quality standards, and effective risk oversight practices.