Data and AI Modeller / Analytics Engineer

• Design and build governed, reusable global data models (gold layer, semantic models, certified data products) in Microsoft Azure Fabric to power consistent reporting, self-service analytics, and AI/ML at scale.
• Enable AI and LLM consumption of data by designing semantic models purpose-built for generative AI, applying RAG principles, and validating AI-generated outputs for accuracy.
• Produce feature-ready datasets and ML-optimized data products for data scientists and AI engineers, and collaborate on ML pipeline design and model integration.
• Work with executives and domain leads to translate business rules and KPI definitions into trusted, reusable metric logic, and own version control, testing, and governance of semantic models.

This role directly enables RES's mission of affordable, zero-carbon energy by building the data foundation that powers every business decision, AI output, and ML model across a global renewable energy company. High-quality data models accelerate renewable energy deployment and operational efficiency, contributing to a sustainable energy future.

• Deep expertise in semantic data modelling (physical, logical, dimensional, star schema, canonical models) and enterprise architecture.
• Hands-on experience with Microsoft Fabric, Power BI semantic models, Azure Synapse, and the Microsoft data stack.
• Advanced proficiency in SQL and DAX for data transformation, metric calculation, and semantic layer development.
• Practical experience designing data products for LLM, generative AI, or ML consumption, including understanding of RAG architectures, prompt engineering, and AI answer risk.

Competitive salary, benefits, and commitment to professional development. A genuinely global remit with a modern Azure/Fabric stack and active investment in AI tooling. Collaborative, growing data function.

RES is the world's largest independent renewable energy company, with a mission to create a future where everyone has access to affordable, zero-carbon energy.