Principal Data Governance

Position Summary:

The Principal Data Governance Lead serves as the IT delivery owner for enterprise data governance — partnering with the Business Data Governance partner to establish governance policies and standards into technology standards, platform controls, and operational practices across the organization.

This role will lead MDM implementation on Oracle Fusion, establish data governance frameworks from a technology perspective — covering data quality, data lineage, access controls, and compliance — and own the governance tooling and platform standards that enable the enterprise data strategy. Will serve as the primary IT delivery lead within the Data Governance Center of Excellence (CoE), partnering with the Business Data Governance CoE to operationalize governance policies across data platforms.

This is a hands-on leadership role that requires both strategic thinking and execution depth — defining what good data governance looks like technically and driving adoption across IT and business stakeholders at all levels.

Major Duties and Responsibilities:

• Data Governance Framework & Standards:
  Designs and owns the enterprise data governance framework — covering data classification, ownership models, access controls, retention policies, and governance operating procedures. Owns the enterprise business glossary, data catalog, and metadata standards, ensuring consistent definition and lineage visibility across all domains. Partners with Security and Compliance to meet regulatory and audit requirements, continuously maturing the framework as platforms and business needs evolve.
• MDM Implementation & Platform Governance:
  Leads IT delivery of MDM on Oracle Fusion — defining data models, governance rules, hierarchy structures, and stewardship workflows in partnership with business and IT ERP teams. Owns golden record definitions, duplicate resolution rules, and master data lifecycle management across MDM domains. Governs MDM-to-downstream integration patterns ensuring master data flows accurately to EDW, analytics, and operational systems.
• Data Quality, Lineage & Compliance:
  Owns enterprise data quality standards — defining quality dimensions, thresholds, and remediation SLAs across all data domains. Implements end-to-end data lineage tracking from source ingestion through Fivetran, dbt, Snowflake, and BI layers. Ensures governance controls meet compliance and audit requirements; identifies and escalates data quality risks with defined remediation plans.
• Governance Adoption & Enablement:
  Drives enterprise-wide adoption of data governance principles across IT and business stakeholders through training, tooling enablement, and stewardship community building. Develops governance playbooks, stewardship guides, and onboarding documentation. Tracks adoption metrics — data dictionary coverage, stewardship participation, and quality score trends.

Education and Experience:

Required:

• Bachelor’s degree in Information Systems, Data Management, Business Administration, or a related field.
• Minimum of 10 years of progressive experience in data governance, data management, or enterprise information management, with at least 5 years in a strategy-setting role.
• Proven experience establishing or scaling a data governance function in a complex, matrixed organization.
• Demonstrated success in leading cross-functional initiatives involving data quality, metadata management, data cataloging, and stewardship at the enterprise level.

Preferred:

• Master’s degree in a data-related discipline (e.g., Data Science, Information Management, or Business Analytics).
• Experience working in Consumer Packaged Goods (CPG), manufacturing, or supply chain–driven industries.
• Experience deploying governance and quality controls in cloud-based environments, including Snowflake and AWS.
• Background in managing third-party vendors or implementation partners related to data governance technology.
• 5 years in a leadership.

Knowledge and Skills:

Required:

• Deep subject matter expertise in data governance principles and best practices, including data ownership, stewardship, data lifecycle management, and metadata strategy.
• Strong working knowledge of data governance technology tools (e.g., Collibra, Informatica Axon, Alation, Microsoft Purview) and experience driving tool adoption across enterprise teams.
• Understanding of data architecture concepts such as data lakes, data me