About the Role:
Data and AI workflows fail quietly when metadata is wrong. Models train on the wrong data, lineage breaks across system boundaries, and business glossary terms drift away from the definitions analysts rely on. Client places Metadata Governance Leads inside customer AI Governance programs to prevent exactly that.
This role owns the metadata strategy and operating model that sits underneath the client's semantic layer: business glossary, data dictionary, lineage, and the integration patterns that connect them to AI/ML pipelines.
You will work alongside the broader client governance team (Data Governance Lead, Solution Architect, MDM/RDM specialists) to make sure metadata is not a one-time documentation exercise but a sustained capability that AI workflows can rely on. This work is tool-agnostic by design; we deploy and govern across whichever metadata platform(s) the client has adopted.
Key Responsibilities:
- Metadata Strategy: Define the enterprise metadata strategy: scope, prioritization, and the integration model that connects business, technical, and operational metadata to AI/ML workflows.
- Business Glossary: Lead the design, population, and ongoing curation of the business glossary; establish definitions, ownership, approval workflows, and the linkage between glossary terms and downstream data assets.
- Data Dictionary & Catalog: Define standards for data dictionary entries, catalog asset models, and the metadata quality bar required for AI-readiness.
- Lineage: Establish lineage capture standards across systems, pipelines, and AI training datasets; partner with engineering on automated lineage where possible and curated lineage where it is not.
- AI/ML Metadata: Extend metadata coverage to AI-specific assets: training datasets, feature stores, model artifacts, prompts, and evaluation data, and integrate them into the broader catalog.
- Operating Model: Design the metadata stewardship operating model: roles, workflows, SLAs, and the governance forums that approve and maintain metadata over time.
- Tool Enablement: Provide platform-agnostic guidance on metadata tool selection, configuration, and integration; lead implementation work on whichever platform the client has adopted (e.g., Collibra, Alation, Informatica, Atlan, Microsoft Purview, OpenMetadata, custom).
- Stakeholder Engagement: Run workshops, working groups, and training sessions that build business and IT fluency in metadata practices.
- Cross-Workstream Integration: Coordinate with the Data Governance Lead, Solution Architect, and MDM/RDM specialists so metadata work compounds with, rather than duplicates, adjacent governance investments.
Required Qualifications:
- 7+ years in metadata management, data cataloging, or information management roles, with at least 2 years leading metadata programs.
- Deep, tool-agnostic expertise in business glossary, data dictionary, data catalog, and data lineage, including how each is operationalized through stewardship and workflow, not just configured in a tool.
- Hands-on experience implementing metadata capabilities in at least one major catalog/governance platform; comfort transferring those patterns to other platforms.
- Experience extending metadata coverage to AI/ML assets (training data, features, models) or a credible plan for doing so.
- Strong facilitation skills with business, IT, and C-suite stakeholders.
- Excellent written and verbal communication; ability to translate metadata concepts for non-technical audiences.
- BA/BS or advanced degree in Business, Computer Science, MIS, or related field.
Preferred Qualifications:
- Experience across multiple metadata/catalog platforms (Collibra, Alation, Informatica, Atlan, Microsoft Purview, OpenMetadata, etc.).
- Familiarity with semantic layer concepts: ontologies, taxonomies, metric frameworks, and how metadata grounds them.
- Experience integrating metadata systems with MLOps tooling, feature stores, or model registries.
- Background in regulated industries where AI training data lineage is required for audit and compliance.
- Familiarity with metadata standards (Dublin Core, DCAT, ISO 11179) and APIs (OpenLineage, OpenMetadata).
What Success Looks Like:
By the end of this engagement, mapped to client's General Project Methodology, the Metadata Governance Lead will have:
- Envision and Align: Aligned business and IT leadership around the metadata strategy and its connection to AI Governance objectives.
- Discover: Completed a metadata current-state assessment, identified priority assets, and surfaced metadata-quality gaps blocking AI use cases.
- Architect and Plan: Delivered the metadata operating model, glossary/dictionary/lineage standards, and tool-agnostic integration architecture.
- Build and Implement: Stood up an operational business glossary with curated terms and active workflows; established lineage coverage for priority flows, including those feeding AI/ML workloads; extended metadata coverage to AI-specific assets where applicable.
- Sustainability: Embedded metadata stewardship into the day-to-day governance operating model and built client capability so the program continues to mature beyond the engagement.
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