Principal Data Governance

Digi Key is one of the fastest growing distributors of electronic components in the world. In addition to offering the broadest selection of in-stock electronic components and providing the best service possible to customers, employees have access to a highly competitive benefits package. To learn more, visit our .**                                                                      **#
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* Position Overview:

** 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…