RES
Data and AI Modeller / Analytics Engineer
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DATA AND AI MODELLER / ANALYTICS ENGINEER
MAKE POWER FOR GOOD
RES is the world's largest independent renewable energy company. Our mission is simple: a future where everyone has access to affordable, zero-carbon energy. The problems we're solving are among the most important of our generation — and the people working on them are extraordinary.
This is a rare opportunity to join a newly created global role within a growing central data and analytics team. If you want to build the data foundation that the whole business depends on — at global scale, using cutting-edge AI and data tooling — read on.
THE ROLE
As Data and AI Modeller / Analytics Engineer, you'll own the design and build of RES's governed, reusable global data models — translating enterprise data into the business-ready dimensions, facts, and metrics that power consistent reporting, self-service analytics, and AI/ML at scale.
This is a hands-on technical role that sits at the intersection of data engineering, business intelligence, and artificial intelligence. You'll work across gold layer models, semantic models and AI-ready data products in Microsoft Azure Fabric — and you'll actively use LLMs, machine learning, and generative AI both as tools in your own workflow and as capabilities you enable for the rest of the business. The quality of your dimensional and semantic models determines the quality of every AI output, every dashboard, and every business decision that flows from RES's data platform.
WHAT YOU'LL DO
- Design and build governed gold layer models/dimensional and semantic models, and certified data products.
- Design models that support self-service analytics, natural language querying, and AI consumption — documenting metric definitions, calculation rules, filters, and caveats so outputs can be safely used by both people and AI tools.
- Translate business rules, KPI definitions, and reporting logic into trusted, reusable metric logic; ensure consistency across dashboards, reports, and AI-enabled tools.
- Own version control, testing, documentation, and governance of semantic models and metric definitions; identify and replace duplicate, conflicting, or ungoverned metrics with controlled enterprise definitions.
- Apply retrieval-augmented generation (RAG) principles to data product design, enabling AI tools to retrieve accurate, contextualised metric definitions and business logic for queries.
- Use LLMs and prompt engineering to accelerate model documentation, metric definition drafting, data lineage annotation, and consistency checking across large model libraries.
- Stay current with how LLM tooling and agentic AI frameworks consume structured data — and shape RES's semantic layer to be AI-ready as these capabilities evolve.
- Produce feature-ready datasets and ML-optimised data products that data scientists and AI engineers can consume directly.
- Work with executives, business domain leads, and senior IT stakeholders to understand data and model requirements and translate them into agreed, future-proof data models. Lead working groups and regularly present to senior executive stakeholders, negotiating and influencing and building strong partnerships across the globe.
- Partner with data engineers and architects on upstream transformations, data quality, lineage. Collaborate with governance, architecture, system owners, and cyber teams to align models.
- Support migration and rationalisation of existing models.
- Apply Python, SQL, and DAX to build and maintain analytical data products and calculation logic.
- Use automation tooling — such as Power Platform, Power Automate, or equivalent — to streamline modelling workflows and reduce manual effort.
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What You'll Bring
- Data modelling — deep expertise in physical, logical, semantic and dimensional modelling within enterprise architecture.
- Azure Fabric & Microsoft stack — hands-on experience with Microsoft Fabric, Power BI, Azure Synapse, and associated consumption patterns.
- SQL and DAX — advanced proficiency for data transformation, metric calculation.
- LLMs and generative AI — practical experience designing data products and dimensional models for LLM consumption; understanding of RAG architectures, prompt engineering, and AI answer risk in an analytics context.
- ML enablement — experience producing feature-ready datasets, understanding of ML pipeline data requirements, and ability to collaborate effectively with data scientists and AI engineers.
- KPI governance — strong understanding of metric definition, business rules, and calculation logic across multiple products and source systems.
- Data quality & observability — experience embedding data quality, lineage, and traceability into modelling workflows.
- AI answer risk — working knowledge of how LLMs and AI tools can fail when consuming poorly governed data; ability to design models that reduce these risks.
- Stakeholders — able to articulate complex modelling and AI concepts to executive and non-technical audiences; comfortable working as a global lead with significant autonomy. Chair working groups and regularly present to senior executive stakeholders, negotiate, influence and direct outcomes and high value products.
- Automation and scripting — Python and/or automation tooling (such as Power Platform or equivalent) applied to modelling and analytics workflows.
YOUR BACKGROUND
Essential
- Degree in data analytics, data science, computer science, or a related discipline — or equivalent hands-on experience.
- Significant experience in analytics engineering and dimensional/semantic modelling, with evidenced outcomes — including executive-adopted models, measurable efficiency savings, and reduced duplicated logic.
- Proven delivery of reusable data layers that enabled self-service reporting across multiple systems and global domains.
- Advanced SQL and DAX; strong command of dimensional modelling.
- Experience with Microsoft Fabric and Azure data platform components.
- Practical experience designing data products for LLM, generative AI, or ML consumption — including an understanding of how AI tools consume structured data and where they can fail.
- Strong experience working with senior business and IT stakeholders to define and govern enterprise metrics.
- Track record of working with senior stakeholders to translate complex data into clear and prioritised requirements and products that drive business decisions. Able to chair working groups and regularly present to senior executive stakeholders with strong negotiating and influencing skills.
- Experience with global data standardisation — harmonising definitions, taxonomies, and formats across regions.
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Desirable
- Experience with RAG architectures and agentic AI frameworks in a data or analytics context.
- Hands-on experience working alongside data scientists on ML feature engineering or model pipeline design.
WHY RES?
- A genuinely global remit — you'll be the modelling lead for a data platform used across a worldwide renewable energy business.
- Work at the intersection of data, BI, and AI — a rare role where your models directly determine the quality of every AI output, ML model, and executive dashboard across the business.
- A modern, cloud-first stack — Azure, Fabric, Synapse, and active investment in AI tooling.
- A collaborative, growing data function with real scope to shape how analytics and AI evolves at RES.
- Join a brand new, agile, global data and analytics team at the heart of AI, innovation, Next Gen technology and driving innovation for competitive advantage for RES.
- You will have a diverse portfolio and be able to lead on many types of work, honing and developing strong skills in Artificial Intelligence to shape your future career at the forefront of the latest technologies.
- A collaborative, cross-functional data function with architecture, AI, science, analytics, and engineering working closely together.
- Competitive salary, benefits, and commitment to your professional development.
At RES, we celebrate differences as we know it makes our company a great place to work. Encouraging applicants with different backgrounds, ideas and points of view, we create teams who work together to solve complex problems and design practical solutions for our clients. Our multiple perspectives come from many sources including the diverse ethnicity, culture, gender, nationality, age, sex, sexual orientation, gender identity and expression, disability, marital status, parental status, education, social background and life experience of our people.
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