Principal Data Governance

DigiKey•Bloomington, MN

14d•$122,000 - $167,000•Hybrid

About The Position

DigiKey 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 benefits and perks page . ______________________________________________________________________ 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).

Requirements

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
Must be authorized to work in the U.S. without the need for employment-based immigration sponsorship, now or in the future. The employer does not offer immigration sponsorship for this opportunity.

Nice To Haves

Experience implementing responsible AI frameworks and establishing model governance boards.
Certifications (e.g., DAMA, CDMP, CIPP, or cloud certifications).

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.