Senior AI Governance Engineer

Xcel Energy•Minneapolis, MN

4d•$97,600 - $159,100•Hybrid

About The Position

The Senior AI Governance Engineer designs, builds, and owns technical capabilities that operationalize enterprise AI governance, including AI controls, risk assessments, and implementation guidance. The role works across Security, Architecture, Privacy, Legal, and Data Science teams, translating governance policies and regulatory requirements into enforceable technical controls across multiple data domains. It establishes engineering patterns for the team and serves as a technical resource to less experienced colleagues. The role develops data quality frameworks and remediation workflows; automates metadata and lineage capture; creates data classification, access control, tagging, and masking patterns; and builds reusable governance components, libraries, and APIs. It also evaluates tools and platform approaches, leads technical workstreams, mentors engineers, and partners with data and technology stakeholders to communicate design decisions and trade-offs. Xcel Energy supports a hybrid work model. Many roles are expected to work onsite three days per week, Tuesday through Thursday, although location expectations vary by role, team, and business needs.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or a related field, or an equivalent combination of education and experience.
Five or more years of experience in data management, data engineering, or data governance engineering.
Advanced SQL proficiency and demonstrated proficiency in Python.
Demonstrated experience implementing at least one enterprise data management capability end to end, such as data quality, metadata and catalog, master data, or data access control.
Ability to independently design technical solutions and defend design decisions to technical and business stakeholders.
Experience working across multiple data domains or business functions.

Nice To Haves

Experience implementing controls in a SOX-, NERC CIP-, or privacy-regulated environment.
Gas and/or electric utility industry experience.
Certifications such as IAPP AIGP (AI Governance Professional), CIPP/CIPM, CDMP (data management), or CISA/CRISC.
Five to seven or more years in data governance, AI/ML risk, model risk management, IT compliance, or audit.
At least two years working directly with AI/ML or generative AI systems, such as model reviews, use-case intake, or risk assessments.
Experience in a regulated industry such as utilities or energy; familiarity with NERC CIP, FERC, or state public utility commission oversight is especially valuable.
Hands-on use of the NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001 and 23894, and model risk guidance such as SR 11-7.
Working knowledge of the EU AI Act, U.S. state AI laws such as Colorado's, and privacy rules such as CCPA and GDPR.
Understanding of responsible AI principles, including fairness, transparency, explainability, accountability, privacy, and safety.

Responsibilities

Design and implement data quality frameworks, rule sets, and scoring methodologies across multiple data domains, measuring accuracy, completeness, timeliness, validity, consistency, and uniqueness.
Define data quality remediation workflows and escalation models.
Design and implement automated metadata and lineage capture across source systems, transformation layers, and consumption assets.
Define metadata harvesting strategies, integration approaches, and binding models between business glossaries and physical assets.
Develop data classification and access-control patterns, including attribute-based access control policies, tagging automation, and dynamic masking within the enterprise data platform.
Partner with cybersecurity on control design and evidence.
Develop reusable governance components, libraries, and APIs, and establish engineering patterns for the team.
Solve complex data management problems, evaluate tooling, and recommend platform capabilities, configurations, and integration approaches.
Mentor less experienced engineers; review work products and provide guidance on standards, design, and troubleshooting.
Lead technical workstreams within governance programs, including effort scoping, delivery sequencing, and progress reporting.
Partner with data owners, data stewards, platform engineering, enterprise architecture, and cybersecurity to translate governance policy into enforceable technical controls.
Communicate technical designs and trade-offs to non-technical stakeholders.