Data Modeller

Role overview

Data modeling and semantic layer

• Design conceptual, logical, and physical models for Bronze, Silver, and Gold Medallion layers.
• Implement dimensional models and SCD2 and historical tracking techniques.
• Set naming conventions and metadata standards; maintain model documentation.
• Design a semantic layer for business-friendly data access and self-service analytics, using AI tooling for deployment and integration.
• Develop ontologies and taxonomies and maintain semantic consistency across data products and reporting layers.

Knowledge graphs and collaboration

• Build and maintain knowledge graphs for entity relationships and data discovery.
• Integrate graph capabilities with Lakehouse metadata and lineage tools such as OpenMetadata.
• Collaborate with architects and platform engineers on graph scalability and performance.
• Partner with data architects, data engineers, and data quality teams to align model design, implementation, validation, and compliance.

Optimization and standards

• Support schema evolution, partitioning strategies, and performance optimization.
• Create reusable modeling templates and best practices for Data Factory squads.

Required skills and experience

• Strong data modeling experience in Lakehouse or data warehouse environments.
• Proficiency in semantic layer design, ontologies, and knowledge graph development.
• Familiarity with AWS Glue, Apache Iceberg, and Medallion architecture.
• Understanding of metadata management and lineage tools; OpenMetadata is preferred.
• Data modeling tools may include ERwin, PowerDesigner, or similar; semantic tools may include Zaimler, AtScale, or equivalent.
• Knowledge graph frameworks may include RDF, OWL, Neo4j, or similar.
• Strong analytical and conceptual thinking, communication, stakeholder engagement, and collaboration skills.
• Able to work in a fast-paced environment and deliver migration targets; experience with Jira and agile ways of working.

Preferred qualifications

• Seven or more years in data modeling or data architecture is preferred.
• Semantic technologies, knowledge graphs, and financial services domain experience are strong pluses.