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.