The Principal Data Governance owns and advances the enterprise data governance program that underpins analytics and AI across the company. This role coaches Data Domain Owners and Business/IT Data Stewards, establishes policies and standards, and drives a multi-year roadmap that enables high-quality, secure, compliant, and responsibly governed data and AI outcomes.

This role is based in Thief River Falls, MN or Bloomington, MN and follows the company hybrid policy (in-office Mondays and Wednesdays).

Responsibilities:

Enterprise Data & AI Governance (50%)

• Define, implement, and continuously improve the enterprise data governance framework spanning data quality, metadata, lineage, classification, and access controls to support analytics and AI use cases.

• Establish and maintain policies/standards for data lifecycle management (ingest → curate → publish), including golden records, master/reference data, and authoritative systems of record.

• Partner with security, privacy, and legal to enforce data privacy and regulatory compliance (e.g., GDPR/CCPA) and data retention/archiving policies in analytics and AI contexts.

Responsible AI & Model Governance (20%)

• Create guardrails for AI/ML development and use: model documentation (model cards), data provenance, fairness/bias testing, human-in-the-loop controls, explainability, and performance monitoring.

• Define approval workflows and risk tiers for AI solutions (predictive models, GenAI, copilots), including change management and periodic reviews.

• Coordinate with MLOps/Platform teams to ensure governed feature stores, reproducibility, versioning, and incident response for models in production.

Data Quality & Stewardship (15%)

• Stand up stewardship operating model: stewardship councils, data domains, ownership and accountability (RACI), and change control for definitions and metrics.

• Define and track data quality SLAs/SLOs, critical data elements (CDEs), and remediation playbooks; publish scorecards and business-ready documentation (data dictionary, business glossary).

Transformation & Enablement (15%)

• Coach cross-functional teams delivering data-driven transformation (CRM/Sales, Supply Chain, Finance, Digital) to embed governance-by-design rather than as a retrofit.

• Lead communities of practice and training programs for data literacy and AI safety; scale adoption of catalog/lineage, quality monitoring, and access workflows.

• Define integration test strategies and entry/exit criteria for data across platforms during migrations and modernizations without anchoring to a single program.

Required Qualifications

• Bachelor’s degree in Information Management, Computer Science, or related field (Master’s preferred).

• 10+ years in data governance/MDM with proven leadership; experience partnering with enterprise architecture, data engineering, and analytics/AI teams.

• Hands-on knowledge of governance tooling (e.g., data catalog/lineage, quality monitoring, metadata/MDM) and modern data platforms (cloud data lakes/warehouses).

• Demonstrated understanding of AI/ML lifecycle and risks, including model risk management, bias, privacy, and security.

• Excellent communication, facilitation, and stakeholder management across business and technology functions, including C-Suite audience

Preferred

• Experience implementing responsible AI frameworks and establishing model governance boards.

• Certifications (e.g., DAMA, CDMP, CIPP, or cloud certifications).

Success Measures / KPIs

• Coverage of governed domains and critical data elements; % with defined owners/stewards.

• Data quality improvements against baselines; DQ incidents MTTR and SLA attainment.

• AI/ML governance adoption: % models with model cards, bias tests, and monitoring in place.

• Cycle-time reduction for compliant data access and re-use; catalog/lineage adoption metrics.