Senior Data Governance & Responsible AI Lead
New
J
JobgetherData Governance
Based in United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- Stakeholder management
Requirements
- 8+ years of experience in data governance, information governance, analytics governance, enterprise data management, or related leadership roles.
- Proven experience implementing governance programs in complex, cross-functional environments.
- Strong knowledge of data ownership, stewardship models, metadata management, lineage, data quality, auditability, and governance controls.
- Experience working with cloud-based data and analytics platforms.
- Ability to collaborate effectively with engineering teams, business stakeholders, legal and security partners, and executive leadership.
- Strong facilitation, communication, documentation, and stakeholder management skills.
- Experience with responsible AI governance, AI risk management, or AI-enabled operational systems is preferred.
- Familiarity with metadata and catalog platforms such as OpenMetadata, Microsoft Purview, Collibra, Alation, or similar technologies is a plus.
- Understanding of governance and compliance frameworks such as NIST, CJIS, HIPAA, SOC 2, or FedRAMP-related environments is preferred.
- Experience supporting KPI governance, semantic governance, business glossary management, or enterprise reporting standards is advantageous.
Responsibilities
- Lead the implementation and execution of data governance frameworks across data, analytics, and AI products.
- Establish governance workflows covering data ownership, stewardship, approvals, escalation paths, and auditability.
- Coordinate governance activities across multiple stakeholders, including business teams, technical teams, and organizational leaders.
- Define source-system readiness standards, ensuring datasets have clear ownership, access pathways, refresh schedules, lineage visibility, and quality expectations.
- Develop and maintain metadata, catalog, business glossary, and lineage governance standards to improve transparency and trust.
- Partner with engineering teams to strengthen data traceability and visibility across platforms, semantic layers, and intelligence products.
- Lead KPI and semantic governance processes to ensure consistent business definitions, reporting standards, and approved metrics.
- Operationalize responsible AI governance workflows, including AI use-case reviews, risk assessment, privacy considerations, human oversight controls, and monitoring processes.
- Establish governance reporting, scorecards, review routines, and quality controls to support accountability and audit readiness.
- Create governance playbooks, templates, SOPs, and documentation to enable long-term adoption and independent operational ownership.
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