Data Modeler

Opportunity for Data Modeller / Data Designer • This role empowers you to shape end-to-end data ecosystems—accelerating delivery, enhancing data clarity, strengthening operational resilience, and driving organisations toward a more insight-rich, data-enabled future. • You will define the data blueprints and foundational models that underpin how customers in dynamic, data-intensive industries operate, scale, and innovate. • You will design robust, future-ready data models that enable seamless integration, advanced analytics, and AI-driven decision making across complex digital transformation programmes. Essential Skills & Experience - Data Modeller / Data Designer • Experienced Data Modeller, Data Designer, Data Specialist or similar • Proven experience delivering conceptual, logical, and physical data models for cloud data platforms, ideally GCP • Strong hands-on modelling for Big Query (analytical/columnar patterns, denormalization strategy, partitioning & clustering considerations) • Expertise in data modelling approaches: 3NF, dimensional (Kimball), Data Vault, and hybrid patterns for Lakehouse designs • Maintain versioned model artefacts (ERDs, schema scripts, JSON/YAML specs) and change logs; manage controlled evolution of models. • Ability to translate banking domain requirements (Customer, Accounts, Payments, Credit, Risk, Finance) into scalable canonical models • Strong understanding of BigQuery performance and cost optimisation impacts driven by modelling choices (query patterns, storage, scan costs) • Experience designing data products for analytics and reporting with trusted definitions (facts, dimensions, SCD, conformed dimensions) • Proficiency with data modelling tools such as ER/Studio, PowerDesigner, ERWin, SQL Developer Data Modeler, or equivalent cloud-native tools. Key Responsibilities - Data Modeller / Data Designer • 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. • 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.