AI Data Architect

• Define the target architecture and transition roadmap for enterprise data platforms including data warehouses, data lakes or lakehouses, real-time streaming, integration patterns and consumption layers
• Design conceptual, logical and physical data models for priority data products, analytical solutions and AI use cases, with clear definitions, ownership and quality requirements
• Define architecture patterns for data ingestion, storage, transformation, sharing, retention, archival and deletion across batch, streaming and real-time use cases
• Partner with business data owners and Data Governance teams to establish common definitions, metadata, lineage, master and reference data requirements and authoritative sources
• Guide Data Engineers in implementing scalable, reusable and governed data products and rationalising legacy data stores
• Ensure data designs incorporate security classification, access control, privacy, segregation, encryption, retention, resilience and Data Protection Act 2019 requirements by design
• Review data solution designs and proposed changes for data impact, represent data architecture at the Architecture Review Board and document decisions and exceptions
• Maintain enterprise data models, data cataloguing requirements and architecture artefacts and guide delivery squads, Data Engineers, Data Scientists and the AI and Data Architect

Qualifications & Experience:

• Bachelor's degree in Computer Science, Information Systems, Data Management, Engineering, Mathematics or a related field
• At least seven (7) years of experience in data management, data engineering, database design, analytics or architecture, including at least three (3) years in data architecture or a senior data modelling role
• Demonstrated experience designing enterprise data models and modern analytical data platforms
• A relevant master's degree and professional certification in data management, data architecture, enterprise architecture or cloud data platforms are added advantages
• Experience in banking, financial services or another data-intensive regulated environment is preferred

Skills & Competencies:

• Conceptual, logical and physical data modelling
• Data warehousing, lakehouse, data lake and data product patterns
• Metadata, lineage, master and reference data, and data quality
• Architecture governance and standards
• Stakeholder engagement and technical leadership