Data Governance Lead

Position Summary

We are seeking a Data Governance Lead to establish and scale the enterprise data governance framework that powers analytics, AI, and autonomous agents. This role will lead the design and governance of the organization's data catalog, business glossary, data lineage, semantic layer, and trusted data products using platforms such as Atlan, Alation, Microsoft Purview, and related technologies.

The ideal candidate understands that modern governance extends beyond compliance and documentation. They will build the metadata, business context, semantic models, and trust frameworks that enable humans and AI agents to consistently discover, understand, and use enterprise data at scale.

This role will partner closely with Data Engineering, Analytics, AI/ML, Enterprise Architecture, Business Teams, and Product Leaders to create an enterprise knowledge foundation that supports analytics, generative AI, agentic workflows, and data-driven decision making.

Key Responsibilities

Enterprise Data

Governance Strategy

• Define and execute the enterprise data governance roadmap.
• Establish governance processes, operating models, stewardship responsibilities, and accountability frameworks.
• Develop standards for data quality, metadata management, lineage, classification, and access governance.
• Drive adoption of governance practices across business and technology teams.

Metadata &

Knowledge Management

• Lead implementation and optimization of Atlan, Alation, Purview, or similar governance platforms.
• Build and maintain enterprise business glossaries, taxonomies, and knowledge frameworks.
• Establish metadata standards that improve discoverability and trust in enterprise data assets.
• Create governance processes for capturing business context and institutional knowledge.

Semantic Layer &

Data Products

• Define enterprise semantic layer strategy across analytics and AI platforms.
• Partner with domain teams to create governed business metrics and standardized definitions.
• Establish lifecycle management for data products and certified data assets.
• Enable consistent business logic across reporting, analytics, AI solutions, and self-service platforms.

AI & Agent

Readiness

• Establish governance frameworks for AI-ready data assets.
• Create structures that enable AI systems and autonomous agents to understand business context, metrics, definitions, and relationships.
• Define standards for contextual metadata, business rules, semantic relationships, and knowledge representation.
• Partner with AI and Data Science teams to ensure trusted, governed data is available for AI applications.
• Develop governance controls for AI-generated insights and agent-based decision support systems.

Data Quality &

Trust

• Establish enterprise-wide data quality standards, monitoring, and remediation processes.
• Define critical data elements and associated quality metrics.
• Develop trust scores, certification processes, and governance workflows for enterprise data assets.
• Drive continuous improvement of data reliability and usability.

Lineage & Impact

Analysis

• Build end-to-end visibility of data lineage across enterprise platforms.
• Establish impact analysis processes for changes to critical data assets.
• Improve transparency into how data moves from source systems through analytics and AI applications.
• Support compliance, audit, and operational risk management requirements.

Stakeholder

Leadership

• Partner with business leaders to define ownership and stewardship for critical data domains.
• Facilitate governance councils and cross-functional data communities.
• Drive adoption and organizational change management initiatives.
• Influence senior leaders on the strategic value of governance, semantic layers, and AI-ready data foundations.