Enterprise Data Governance Leadership Define and own the Enterprise Business Data Governance strategy across master data, reference data, transactional data, analytical data, and AI-relevant datasets, aligned with regulatory requirements, Company priorities, transformation initiatives, and AI readiness. Establish Data Governance as an enterprise-wide capability embedded into business-as-usual processes, decision forums, transformation initiatives, and operational routines rather than as a separate project or overlay. Act as the single point of accountability for enterprise data governance frameworks, standards, governance forums, decision-making, and escalation, while ensuring business domains retain accountability for data quality, remediation, and outcomes Governance Framework & Policy Ownership Develop, maintain, and enforce the enterprise data governance framework, including policies, standards, definitions, and decision rights. Define clear ownership boundaries and interfaces with Data Privacy, Information Security, Risk, Compliance, and Enterprise Architecture, ensuring governance responsibilities are complementary and not duplicative. Establish and operate governance councils, stewardship forums, and escalation mechanisms to support enterprise decision-making, adoption, and sustainable business ownership. Operating Model & Capability Enablement Design, implement, and operate a federated Hub-and-Spoke data governance operating model that balances central standards with business domain accountability and execution.



Ensure governance execution capabilities remain embedded within business domains, with transparent ownership for data quality and outcomes Drive change management, communications, training, and stewardship enablement to build data governance capability and adoption across business units Data Quality, Risk & Performance Management Identify, assess, and mitigate enterprise data risks impacting regulatory compliance, operational performance, analytics, data traceability, provenance, and AI use cases. Define, track, and report measurable KPIs, including percentage of domains with appointed data owners, data quality improvements, governance adoption, remediation timelines, stewardship coverage, and maturity progression across domains. Ensure the governance model scales efficiently without unnecessary organizational complexity or cost overhead. Transition to Business Ownership Lead and execute a time-bound transition plan from Technology-led to Business-owned data governance, including phased milestones, readiness criteria, accountability transfer points, funding and resourcing assumptions, and the long-term governance ownership model. Prepare business leaders, data owners, and data stewards to fully assume accountability through role clarity, readiness assessments, adoption planning, training, and stewardship enablement. Embed data governance practices into business-as-usual operations and domain decision-making, ensuring sustainability, measurable adoption, and clear business ownership beyond the interim phase.

📌 Data Governance Manager (India)
🏢 WhiteSlips Job Management Consultants
📍 India