Lead end-to-end design and review of enterprise conceptual, logical and physical data models, define modelling standards and build scalable data structures aligned with business requirements.
Conduct design reviews for data warehouses and data marts to identify modelling gaps, redundancies and performance bottlenecks, and drive data warehouse modernisation. Collaborate with architecture, engineering and BI teams to optimise data models for data lake, lakehouse and cloud data warehouse platforms, with clear understanding of modelling differences across traditional warehouse, data lake and lakehouse environments.
Support semantic layer design, self-service analytics and enterprise KPI standardisation to improve reporting performance. Enable data governance via modelling practices, maintain metadata and data lineage, improve data consistency and data quality. Facilitate modelling workshops and design reviews, engage business and technical stakeholders, and deliver data models, assessment reports, data dictionaries and related documentation.
Requirements
8+ years of hands-on experience in data warehousing, analytics and enterprise data modelling. Strong expertise in dimensional modelling including star/snowflake schema, fact/dimension tables and SCD, plus conceptual, logical and physical data modelling.
Solid knowledge of cloud lakehouse platforms; experienced supporting BI tools such as Power BI or Tableau. Familiar with data lineage, metadata management, data quality and master data management concepts.
Able to work cross-functionally and independently deliver modelling reviews and solutions.
Preferred
Background in financial services, banking or insurance.
Hands-on experience with Azure data ecosystem.
Experience working with vendors and system integrators.