Data Modeller

Why this role exists:

The Data Modeller provides the foundational structural design for Officeworks data assets, ensuring information is organised, accessible, and high-performing across enterprise platforms. By designing robust logical and physical models for Snowflake and Datasphere, this role enables the business to transition toward a self-serve analytics model and democratised data access. The role plays a critical part in the execution of the enterprise data strategy, bridging the gap between raw data ingestion and the delivery of curated, semantic layers that power advanced analytics and AI initiatives.

Where you will make a difference:

In this role you will:

Data Architecture & Modelling:

• Design and maintain complex logical and physical data models for the Snowflake enterprise data platform and SAP Datasphere.
• Define and develop curated and semantic layers to ensure data is structured for optimal business consumption.
• Ensure all data models align with established canonical models and the enterprise business glossary to maintain consistency across the Technology function.

Performance & Optimisation:

• Optimise data models and structures to ensure high performance, scalability, and cost-efficiency within cloud environments.
• Support data lineage and metadata integration to ensure transparency, traceability, and trust in data assets.
• Collaborate with Data Engineers and Architects to ensure physical implementation aligns with architectural intent and performance requirements.

Process Improvement & Continuous Improvement:

• Proactively identify opportunities to streamline data modelling workflows and improve the quality of the data lifecycle.
• Drive continuous improvement initiatives within the Data & AI team to enhance operational efficiency and technical documentation standards.
• Contribute to the evolution of data modelling standards and best practices at Officeworks, ensuring they remain fit-for-purpose as the AI and data landscape evolves.

Who you will be working with:

• Data Team: Collaborate with Engineers, Architects, and the Data Platform Manager to deliver integrated data solutions.
• Analytics Teams: Partner with Data Scientists and Analysts to ensure data structures support advanced analytical models.
• Business Stakeholders: Interact with functional partners to understand reporting requirements and translate them into semantic data models.
• Quality Teams: Engage with QA Associate Analysts to ensure data models meet rigorous enterprise standards and governance requirements.

What success looks like:

• Model Integrity: Data models are accurately aligned with the enterprise canonical model and business glossary.
• Platform Performance: Snowflake and Datasphere environments operate efficiently with optimised query performance and structured semantic layers.
• Operational Excellence: Consistent delivery of high-quality documentation and improved data modelling processes within the Data & Analytics hub.
• Self-Serve Enablement: Successful delivery of data assets that enable business users to perform self-serve analytics with minimal intervention.

How you will lead:

Individual Contributor:

• Lives our Officeworks values and behaviours
• Proactively contributes to a safe working environment, escalates appropriately if there are unsafe conditions or inappropriate behaviour
• Operates in line with applicable Officeworks company policies and Code of Conduct
• Demonstrates a strong sense of personal accountability and curiosity to learn and develop

Qualifications and work experience:

Essential:

• Education: Bachelors degree in Computer Science, Mathematics, or a related field.
• Experience: Minimum of 3-5+ years in Data Modelling or Data Engineering roles, with a focus on designing enterprise-scale data warehouses.
• Technical Expertise: Proven experience in logical and physical data modelling, specifically for Snowflake and SAP data environments.
• Platform Knowledge: Hands-on experience with Snowflake, GitHub, and modern data warehouse architectures (Medallion/Layered architecture).
• Adaptability: Demonstrated ability to adapt technical skills from traditional Machine Learning environments to emerging AI-driven data requirements.
• Analytical Skills: Strong ability to translate complex business requirements into scalable technical data structures.

Preferred:

• Industry Experience: Experience within a large-scale Retail or complex commercial environment is highly regarded.
• Advanced Systems: Familiarity with SAP BW migration projects or SAP Datasphere.
• Tooling: Experience with enterprise data modelling tools and metadata management platforms.