Data Modelling Architect

Key Roles & Responsibilities

• Design and maintain conceptual and logical data models for the Unified Data Platform (UDP), ensuring they meet business needs and data governance standards.
• Design and develop scalable data solutions across the silver and gold layers, creating trusted and reusable datasets for analytics, reporting, and AI use cases.
• Define and maintain data architecture standards, modelling patterns, and best practices to ensure data is consistent, governed, and ready for business consumption.
• Work closely with engineers, product owners, business analysts, and stakeholders to understand requirements and translate them into effective data solutions.
• Support the migration of data from legacy platforms (such as Oracle and Teradata) into the new cloud-based Unified Data Platform using modern Lakehouse and Data Vault approaches.
• Implement data governance standards, including metadata management, lineage and data quality rules, to ensure "Gold Standard" datasets.
• Provide architectural consultancy to agile delivery squads, ensuring solutions adhere to architectural policies and practices.
• Continuously learn and apply modern data architecture, cloud, modelling, analytics, and AI best practices.
• Demonstrate curiosity, innovation, and a growth mindset while contributing to the organisation's data transformation journey.

Job Requirements, Knowledge & Experience

• Data modelling, Data warehousing or analytics engineering, with a strong understanding of data modelling concepts. Exposure to Data Vault 2.0 and an interest in learning Data Vault methodologies would be advantageous.
• Good understanding of modern data platforms, Lakehouse architecture, cloud technologies and data warehousing concepts.
• Expertise working with enterprise data platforms and integrating data from multiple source systems.
• Experience supporting data platform modernisation, migration or transformation initiatives is advantageous.
• Understanding of data governance practices, including metadata management, data lineage, and data quality controls.
• Strong SQL and data transformation skills, with experience creating datasets that support reporting, analytics, and self-service data consumption.
• Ability to translate business requirements into practical, scalable, and maintainable data solutions.
• Experience working with business and technical stakeholders to deliver data-driven solutions.
• Knowledge of BigQuery, GCP, dimensional modelling, semantic models, or modern analytics platforms would be beneficial.
• Interest in modern data architecture, AI, machine learning, and emerging data technologies.
• Strong problem-solving, communication, and stakeholder management skills.
• Demonstrates curiosity, a growth mindset, and a willingness to learn new technologies, tools, and modelling approaches.

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