Snowflake Data Modeller

The Snowflake Data Modeller will be responsible for designing, maintaining, and evolving enterprise data models within a Snowflake environment. The role will involve close collaboration with Data Architects, Data Engineers, Business SMEs, Data Owners, Product Managers and Analytics teams to ensure that data structures are scalable, consistent, governed, and fit for business use.

Key responsibilities:

• Design and maintain conceptual, logical, and physical data models aligned with enterprise data architecture standards.
• Translate business and technical requirements into robust, scalable, and reusable data models.
• Design data models specifically for Snowflake data platforms and data warehouses.
• Apply appropriate modelling methodologies, including Kimball, Star Schema, Data Vault, and normalized data models.
• Define and document entities, attributes, relationships, keys, hierarchies, and business rules.
• Design and structure data across Bronze, Silver, and Gold layers in a Medallion Architecture.
• Develop models that support data ingestion, integration, transformation, analytics, reporting, AI, and data products.
• Collaborate with Data Engineers to ensure that logical and physical models can be efficiently implemented in Snowflake.
• Ensure consistency and alignment of data models across different business domains and data products.
• Contribute to the definition and implementation of enterprise data standards and modelling conventions.
• Support metadata management and data lineage by ensuring that model definitions and relationships are properly documented.
• Work with semantic layers and Common Information Models (CIM) to ensure consistent business definitions and data consumption.
• Apply data governance principles throughout the data modelling lifecycle.
• Support data quality, validation, and reconciliation activities to ensure the accuracy and integrity of data structures.
• Analyse existing data models and identify opportunities for simplification, standardisation, optimisation, and reuse.
• Maintain accurate and up-to-date data model documentation and modelling artefacts.
• Use appropriate data modelling tools to create, maintain, and communicate data models.
• Work closely with stakeholders to resolve modelling issues, clarify requirements, and ensure alignment between business needs and technical implementation.
• Contribute to the continuous improvement of the organisation's data architecture, modelling standards, and governance practices.

Key Technologies:

• Snowflake
• SQL
• Data Modelling
• Kimball / Dimensional Modelling
• Star Schema
• Data Vault
• Normalized Data Models
• Medallion Architecture
• Metadata Management
• Data Lineage
• Semantic Layers
• Common Information Models (CIM)
• Data Governance
• Data Quality & Reconciliation
• Data Modelling Tools.