Data Governance Lead

Duration: 6 to 8 months

Working Hours/Time Zone: 8 hrs/day, UAE time (following the UAE labour law)

Key Skills: Experience on Leading Data Governance for enterprise environments, Good communication skill

Data & Analytics \| AI Platform on Azure

We are seeking a highly experienced and driven Data Governance Lead / Senior Manager to join our Data & Analytics team, operating on a modern Azure-based AI Platform.

This is a critical role responsible for

defining, implementing, and sustaining enterprise-wide data governance across complex, multi-domain data environments.

The ideal candidate will bring deep hands-on expertise in data governance frameworks — particularly DAMA-DMBOK — and will have a proven track record of building governance programs from the ground up in large-scale enterprise settings.

You will work closely with data owners, stewards, platform architects, compliance teams, and executive stakeholders to embed a culture of data trust, quality, and accountability.

▸Design and implement an enterprise-wide Data Governance framework aligned to DAMA-DMBOK

standards and industry best practices.

▸Establish governance operating models including Data Domains, Ownership structures, Stewardship councils, and Policy hierarchies.

▸Define governance charters, policies, standards, and procedures across the full data lifecycle — ingestion through consumption.

▸Drive alignment between business strategy and data governance objectives to ensure measurable

business value.

▸Lead data governance implementation on Microsoft Azure using tools such as Microsoft Purview, Azure Policy, Unity Catalog, and Synapse Analytics.

▸Establish data cataloging, lineage tracking, classification, and sensitivity labeling across Azure Data Lake, Synapse, and Databricks environments.

▸Define and enforce role-based access control (RBAC), data masking, and entitlement policies on the AI and Analytics platform.

▸Partner with platform and cloud engineering teams to embed governance guardrails into CI/CD pipelines and data product delivery.

▸Define and monitor data quality dimensions: Accuracy, Completeness, Consistency, Timeliness,

Uniqueness, and Validity.

▸Implement data quality rules, KPIs, and scorecards — driving continuous improvement across critical data domains.

▸Own end-to-end metadata management strategy including business glossary, data dictionaries, and technical metadata repositories.

▸Establish data lineage documentation and impact analysis processes for regulatory and operational compliance.

▸Ensure data governance policies align with regulatory requirements - GDPR, DPDP Act, SOX, HIPAA, or applicable sectoral standards.

▸Collaborate with Legal, Risk, and Compliance teams to assess data-related risks and define mitigation controls.

▸Lead data ethics principles across the AI platform - governing responsible AI data usage, bias mitigation, and model explainability standards.

▸Support internal and external audits; maintain governance evidence and traceability documentation.

▸Establish and chair the Data Governance Council comprising business data owners, data stewards, IT, and executive sponsors.

▸Champion a data-driven culture through training, awareness programs, and governance communication strategies.

▸Define and manage the RACI model for data accountability across business units and data domains.

▸Act as the primary point of escalation for data governance issues, disputes, and policy exceptions.

▸Define governance maturity metrics and track progress using established models (e.g., CMMI, Gartner Data Governance Maturity Model).

▸Publish governance dashboards and scorecards for executive leadership, including data quality trends and compliance posture.

▸Conduct regular data governance health assessments and recommend corrective actions.

▸5 to 10 years of hands-on experience in Data Governance, with at least 3 years in a leadership or program ownership role.

▸Demonstrated experience setting up data governance programs from scratch in an enterprise

environment.

▸Deep knowledge of the DAMA-DMBOK framework and its practical application across data governance domains.

▸Proven experience with Microsoft Purview or equivalent enterprise data catalog / governance tools.

▸Strong understanding of data architecture concepts: Data Lakes, Data Warehouses, Data Mesh,

Lakehouse.

▸Azure ecosystem: Microsoft Purview, Azure Synapse Analytics, Azure Data Lake Gen2, Azure Active Directory, Azure Policy.

▸Data catalog and lineage tools: Apache Atlas, Collibra, Alation, or Informatica CDGC.

▸Data quality tools: Great Expectations, Informatica DQ, Azure Data Quality, or equivalent.

▸Proficiency in SQL for data profiling, lineage queries, and quality rule definition.

▸Familiarity with Databricks Unity Catalog for lakehouse governance.

▸Understanding of API-driven data governance integrations and metadata automation.

▸In-depth knowledge of DAMA-DMBOK v2 knowledge areas: Data Quality, Metadata, Master Data, Data Security, Data Architecture.

▸Awareness of DCAM, TOGAF, or COBIT frameworks as they relate to data governance.

▸Working knowledge of GDPR, India DPDP Act 2023, SOX, or sector-specific data regulations.

▸Experience governing AI/ML data pipelines and ensuring responsible data practices.

▸Exceptional stakeholder management and executive communication skills.

▸Ability to influence without authority - driving adoption across diverse business units.

▸Strong analytical thinking and the ability to translate complex governance concepts into actionable outcomes.

▸Experience facilitating governance councils, working groups, and cross-functional data forums.

▸Certified Data Management Professional (CDMP) — Associate or Practitioner level.

▸Microsoft Certified: Azure Data Engineer Associate or Azure Solutions Architect.

▸Experience with data mesh architecture and federated data governance models.

▸Prior exposure to AI platform governance — governing feature stores, model registries, or ML pipelines.

▸Experience in Financial Services, Healthcare, Retail, or Telecom verticals with domain-specific data regulatory requirements.

▸Familiarity with data contract frameworks and data product governance in modern data platforms.