Data Modeller

- Define and maintain conceptual, logical, and physical data models that accurately reflect business processes and support analytics, AI/ML, and operational needs. - Translate business requirements into robust data entities, attributes, relationships, and constraints; ensure traceability from requirements to models. - Establish and enforce GDM modelling standards and naming conventions (e.g., normalization, dimensional/star/snowflake patterns, data vault where appropriate). - Design dimensional models (facts, dimensions, hierarchies, slowly changing dimensions) for BI/analytics and performance at scale. - Create and manage canonical data models and semantic layers to enable consistent metrics and self-service analytics across domains. - Ensure data quality by defining integrity rules, reference/master data relationships, and validation checks embedded in pipelines. - Optimise models for performance and cost (partitioning, clustering, indexing, compression, surrogate keys, distribution strategies). - Drive data integration design across sources (CDC, event streaming, APIs), mapping source-to-target, resolving conflicts, and handling historical changes. - Support AI/ML readiness by modelling features, aggregations, and histories; collaborate on feature stores and model input/output schemas. - Embed privacy and security requirements into models (PII classification, minimisation, masking, role-based access, retention, and residency).