Senior AI Governance Engineer

Sourced from roleweek.com

Xcel Energy is hiring a Senior AI Governance Engineer to design and own the technical capabilities that operationalize enterprise AI governance. The role translates governance policy and regulatory requirements into enforceable technical controls across data quality, metadata/lineage, and data classification and protection. You will partner with Security, Architecture, Privacy, Legal, and Data Science teams and mentor less experienced engineers.

• Design and implement data quality frameworks, rule sets, and scoring methodologies across data domains
• Build metadata and lineage capture automation spanning source systems, transformation layers, and consumption assets
• Develop data classification and access control patterns, including attribute-based access control, tagging automation, and dynamic masking
• Create reusable governance components, libraries, and APIs and establish engineering patterns for the team
• Lead technical workstreams within governance programs, including scoping, sequencing, and progress reporting
• Mentor less experienced engineers and review work products
• Partner with data owners, stewards, platform engineering, architecture, and cyber security to translate policy into enforceable controls

• Bachelor's degree in Computer Science, Information Systems, Data Analytics, Engineering, or related field, or equivalent experience
• 5+ years in data management, data engineering, or data governance engineering
• Advanced SQL proficiency and demonstrated Python proficiency
• Experience implementing at least one enterprise data management capability end to end (data quality, metadata/catalog, master data, or data access control)
• Ability to design technical solutions independently and defend design decisions to technical and business stakeholders

• Experience implementing controls in a SOX, NERC CIP, or privacy-regulated environment
• Utility industry experience
• Certifications such as IAPP AIGP, CIPP/CIPM, CDMP, or CISA/CRISC
• 2+ years working directly with AI/ML or generative AI systems (model reviews, use-case intake, risk assessments)
• Hands-on use of NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, and SR 11-7
• Knowledge of the EU AI Act, U.S. state AI laws, CCPA, and GDPR
• Understanding of responsible AI principles: fairness, transparency, explainability, accountability, privacy, and safety