Lead Data Architect - Enterprise Data & AI

About the role

A leading organisation in Abu Dhabi, investing heavily in its enterprise Data & AI capabilities, is looking for an experienced Lead Data Architect. The role owns enterprise data architecture, defining the models, standards and architectural patterns that let data be used effectively across analytics, AI and Agentic AI systems. It is a senior, hands-on position working across Data Engineering, AI, Analytics and business teams to build scalable, well-governed and trusted data foundations.

Key responsibilities

• Lead the design of enterprise-level logical and physical data models across complex business and operational environments.
• Define data architecture, integration and interoperability standards across multiple systems and data sources.
• Establish reusable architectural patterns for data ingestion, transformation and consumption.
• Own architecture governance and ensure solutions meet enterprise, security and compliance standards.
• Embed data quality, MDM, lineage, metadata and data stewardship practices.
• Develop metadata frameworks, data dictionaries and semantic layers for consistent, trusted data definitions.
• Design data structures and access patterns optimised for LLMs, Machine Learning and Agentic AI systems.
• Guide Data Engineers and work with AI, Analytics and business teams to turn requirements into scalable architecture.

Required profile

• 10-12+ years of experience in Data Architecture, Data Modelling, Enterprise Data Design or Data Governance, at Lead or Principal level.
• Proven track record delivering enterprise data architectures across complex, multi-source environments.
• Bachelor's or Master's degree in Computer Science, Engineering or a related technical discipline.
• Experience in financial services, investment organisations or similar complex enterprises is advantageous.

Required skills

• Conceptual, logical and physical data modelling, including dimensional and Data Vault approaches.
• Cloud platforms such as Azure, AWS or GCP.
• Modern data platforms and lakehouse technologies such as Databricks, Microsoft Fabric or Snowflake.
• Data integration, APIs and event-driven/streaming architectures.
• Metadata, lineage and governance tools such as Unity Catalog, Collibra or Alation, and MDM platforms.
• Strong SQL and Spark across relational and NoSQL environments.
• Ability to support AI, ML and Agentic AI workloads.