Data Governance Consultant

** Due to client requirements, applicants must be able to work on a w2 basis

We are seeking an experienced Data Governance Lead to help establish, implement, and scale an enterprise-wide data governance program supporting an evolving AI/ML and data modernization strategy. This is a high-impact opportunity for a hands-on professional who can develop governance frameworks, improve data quality and accessibility, and drive adoption across business and technology teams.

Ideal candidates will bring 5+ years of enterprise data governance experience, strong SQL proficiency, and hands‑on experience with metadata management, data catalogs, and governance processes. Experience supporting AI/ML environments, cloud data platforms, and regulated industries will be especially valuable.

• 5+ years of experience in data governance, enterprise data management, or related disciplines, preferably within complex, multi-system environments.
• Strong SQL proficiency with the ability to analyze, validate, and investigate enterprise data.
• Experience developing or implementing governance policies, standards, controls, and operating procedures.
• Hands‑on experience with data inventories, metadata standards, classification, tagging, ownership, and catalog management.
• Understanding of data quality controls, lineage tracking, data lifecycle management, and governance validation processes.
• Proven ability to partner with business stakeholders, IT, data engineering, analytics, and risk or compliance teams.
• Ability to influence stakeholders, establish accountability, and promote consistent governance practices across an organization.

The immediate priority is to strengthen enterprise data governance capabilities while supporting the development and operationalization of an AI/ML platform.

You will work closely with data leadership, analytics teams, and technology stakeholders to:

• Establish scalable governance practices that improve data transparency, accountability, accessibility, and trust across enterprise systems.
• Support ML model governance by implementing documentation, metadata, review, approval, and production deployment controls.
• Improve enterprise data catalogs through standardized metadata, ownership assignments, certification processes, and asset lifecycle management.
• Drive governance adoption by creating practical standards, training materials, and processes that support business and technical users.
• Strengthen responsible AI practices by supporting data privacy, regulatory compliance, model governance, and risk management initiatives.

• Develop and implement enterprise data governance frameworks, including policies, standards, metrics, procedures, and accountability structures.
• Support AI/ML platform governance by establishing processes for model registration, metadata documentation, approvals, version tracking, and production readiness.
• Evaluate and enhance data catalogs to improve metadata completeness, asset discoverability, ownership, certification, and ongoing maintenance.
• Standardize metadata management by developing tagging conventions, documentation templates, validation rules, and quality controls for datasets, reports, dashboards, and analytics assets.
• Establish lifecycle governance for data and analytics assets progressing from proof of concept through development and production, including approval checkpoints and ownership requirements.
• Implement data lineage practices to improve traceability between source systems, transformations, analytics platforms, and downstream applications.
• Partner with security, risk, and compliance teams to address data privacy, responsible AI, regulatory obligations, and potential data exposure or leakage.
• Evaluate and…