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# Data Governance and AI Governance strategies

- **Company:** Capgemini
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Salary:** $117,930.0 - $223,591.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Architecture, Data Governance, Graph Database, Meta-Data Management, Scrum Methodology, Enterprise Data Management, Google Cloud, Retrieval-Augmented Generation, Multi-Agent Systems, Generative AI, Agentic-AI, Togaf, Data Lineage, Collibra, Data Management, Databricks
- **Published:** November 4, 2026
- **Apply:** https://www.capgemini.com/jobs/576409-en_GB_SAPBTP/x/

## About the Role

* 10+ years in Data Governance, Enterprise Data Management, Data Architecture, AI Governance, or Consulting.
 * Proven leadership of enterprise transformation programs and governance implementations.
 * Deep expertise in Governance Frameworks, Data Quality, Metadata Management, MDM, Data Lineage, Privacy, AI Governance, Responsible AI, and Enterprise Architecture.
 * Knowledge of Generative AI, Agentic AI, RAG, Knowledge Graphs, Agent Orchestration, AI Monitoring, and Security Controls., * Operate as a senior architecture lead and trusted advisor.
 * Translate strategy into governance solutions, roadmaps, and measurable outcomes.
 * Lead stakeholder alignment and enterprise decision-making.
 * Mentor teams, build reusable assets, support proposals, and contribute thought leadership.
 * Balance innovation, compliance, cost, risk, speed, and business value.

Preferred Certifications

 * DAMA CDMP
 * EDM Council DCAM
 * TOGAF Enterprise Architecture
 * IAPP AIGP
 * ISO 42001 Lead Implementer/Auditor
 * NIST AI Risk Management Framework
 * Collibra, Informatica, Microsoft Purview, Databricks, IBM, AWS, Azure, or Google Cloud certifications
 * PMP, Agile/Scrum, Privacy, Security, Risk, and Change Management certifications

Success Measures

 * Successful adoption of governance and AI governance frameworks.
 * Improved data quality, metadata, lineage, compliance, and audit readiness.
 * Increased AI readiness and controlled AI adoption.
 * Strong stakeholder engagement and measurable business value.
 * Demonstrated ability to architect and govern enterprise-scale data and AI ecosystems.

## Description

A senior enterprise architect and trusted advisor responsible for defining and leading enterprise-wide Data Governance, AI Governance, Data Management, and Architecture strategies. The role focuses on establishing governance frameworks, operating models, platforms, controls, and AI governance practices that enable organizations to scale data and AI capabilities safely and effectively.

Your Role :

 * Define enterprise data governance and AI governance strategies, operating models, roadmaps, KPIs, business cases, and value realization plans.
 * Advise executive stakeholders including CDO, CIO, CTO, Risk, Privacy, Security, Compliance, and Business Leaders.
 * Lead governance maturity assessments, capability design, architecture reviews, platform evaluations, and implementation roadmaps.
 * Own architecture, quality, risk, and delivery across governance, catalog, data quality, privacy, MDM, analytics, cloud, and AI initiatives.
 * Architect and implement governance platforms: Collibra, Informatica IDMC/CDGC, Alation, Ataccama, Microsoft Purview, Atlan, Databricks Unity Catalog, IBM watsonx.governance.
 * Design metadata management, lineage, business glossaries, stewardship, data quality, certification, privacy, retention, and data-product governance capabilities.

AI & Agentic AI Leadership :

 * Use GenAI and AI agents to automate metadata discovery, glossary creation, lineage analysis, anomaly detection, issue remediation, and governance monitoring.
 * Design AI governance frameworks covering AI intake and risk assessment, RAG readiness, model inventories, monitoring, incident management, recertification, and retirement.
 * Establish Responsible AI controls for fairness, transparency, explainability, privacy, security, traceability, and human oversight.
 * Design agentic AI environments with confidence thresholds, approval workflows, exception handling, rollback mechanisms, and accountable ownership.