Data Transformation Lead

Role Overview

We are seeking a senior-level Data Transformation Lead to oversee the strategic data initiatives for a newly established AI Lab within a prominent banking institution based in Abu Dhabi. This leadership role centers on defining and executing data strategy, governance frameworks, operating models, and transformation programs essential to support scalable AI, analytics, and digital product deployment across the bank.

Key Responsibilities

• Design and manage the enterprise-wide Data Strategy and Transformation roadmap aligned with the bank's overarching technology, AI, and digital objectives.
• Develop and implement a comprehensive data governance framework covering ownership, accountability, data quality, lineage, metadata, standards, and control mechanisms.
• Refine and evolve the organization’s Data Operating Model, delineating roles, responsibilities, decision-making rights, and collaborative workflows.
• Foster robust Data Ownership and Stewardship across both business and technology divisions.
• Lead initiatives to enhance data quality, accessibility, consistency, and trustworthiness throughout the enterprise.
• Create frameworks addressing data governance, data products, critical data elements, and domain management.
• Collaborate with AI Lab and technology leaders to ensure data strategies effectively underpin AI, generative AI, advanced analytics, and machine learning capabilities.
• Steer cross-functional data transformation projects to eliminate silos and promote enterprise data reuse.
• Establish and enforce data governance policies, standards, and controls in compliance with regulatory and risk management requirements.
• Set up relevant KPIs, dashboards, and executive-level reports monitoring data quality and governance performance.
• Coordinate with Risk, Compliance, Information Security, Technology, and business units to ensure adherence to regulated banking data management standards.
• Influence organizational leadership to adopt new data standards and updated operating models.
• Evaluate and prioritize data transformation programs based on business impact, AI preparedness, and strategic importance.
• Build and lead high-caliber teams specializing in data strategy, governance, and transformation.

Core Focus Areas

• Data Strategy: Enterprise data strategy, transformation roadmap, maturity assessments, architecture aligning, data product planning, AI/GenAI readiness.
• Data Governance: Ownership, stewardship, quality control, metadata management, data classification, critical element identification, governance frameworks and policies.
• Operating Model: Data domain ownership, governance councils, business and technology integration, accountability, decision rights, change management.

Ideal Candidate Profile

• Over 15 years of experience in senior roles encompassing Data Strategy, Transformation, Governance, or enterprise data leadership.
• Proven record of directing large-scale enterprise data transformation initiatives.
• Expertise in designing and implementing Data Strategy and Governance frameworks within complex settings.
• Strong knowledge of modern data architectures and platforms, enabling effective collaboration with Data Architects, Engineers, and AI specialists.
• Experienced in developing data operating models, including establishing governance roles and stewardship.
• Deep understanding of data quality management, metadata, lineage, master data, and data management principles.
• Adept at engaging and influencing senior business and technology leaders.
• Superior strategic and commercial acumen linking data investments to business value.
• Background within banking, financial services, fintech, or similarly regulated industries is highly preferred.
• Familiarity with regulatory data standards such as BCBS 239 is a distinct advantage.
• Exposure to AI/GenAI transformations and strategies enabling AI-ready data ecosystems is desirable.

Candidate Differentiators

The ideal applicant will be a visionary data transformation leader beyond standard Data Governance or purely technical roles. Candidates should demonstrate success in building and enacting enterprise-wide data strategies, governance structures, and measurable organizational transformation. Comfort operating at VP or C-suite levels, along with technical proficiency to guide and challenge data architecture and AI teams, is essential. A combination of strategic leadership, governance expertise, transformation experience, and banking domain knowledge supporting enterprise-scale AI capabilities is expected.