Description

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 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

Semantic Modelling & Data Products

• Design and build governed gold layer models, semantic models, and certified data products — including dimensional models, canonical models, and reusable semantic structures across enterprise domains.
• Translate business rules, KPI definitions, and reporting logic into trusted, reusable metric logic; ensure consistency across dashboards, reports, and AI-enabled tools.
• 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.
• 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.

AI & LLM Enablement

• Design and maintain semantic models purpose-built for LLM and generative AI consumption — ensuring AI agents, copilots, and natural language querying tools access only certified, well-governed definitions rather than raw or ambiguous data.
• Apply retrieval-augmented generation (RAG) principles to data product design, enabling AI tools to retrieve accurate, contextualised metric definitions and business logic at query time.
• Validate AI-generated analytical outputs against correct metric logic, approved filters, and certified semantic models — actively identifying and resolving AI answer risks including metric inconsistency, missing context, wrong filters, hallucinated definitions, and unsupported conclusions.
• Use LLMs and prompt engineering in your own workflow 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.

Machine Learning Enablement

• Produce feature-ready datasets and ML-optimised data products that data scientists and AI engineers can consume directly — reducing the data preparation burden and accelerating model development.
• Advise ML teams on data modelling requirements: feature engineering considerations, training/validation data structure, label availability, and the implications of business rule logic on model inputs.
• Design data products that support both batch ML pipelines and real-time or near-real-time inference use cases.
• Collaborate with data scientists to ensure model outputs and predictions are correctly integrated back into the semantic layer — making ML-generated signals available as governed, reusable metrics alongside traditional KPIs.

Stakeholder & Domain Collaboration

• Work with executives, business domain leads, and senior IT stakeholders to understand reporting requirements and translate them into agreed, future-proof data models.
• Partner with data engineers and architects on upstream transformations, data quality, lineage, and master data management.
• Collaborate with governance, architecture, system owners, and cyber teams to align models to metadata, ownership, and certification standards.
• Support migration and rationalisation of existing Power BI datasets, measures, and legacy reporting logic.

Automation & Engineering

• 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.
• Apply LLMs and prompt engineering where they accelerate modelling, documentation, or metric validation work.
• Optimise Azure semantic models for performance, quality, and scalability.

What You'll Bring

• Semantic data modelling — deep expertise in physical, logical, and dimensional modelling within enterprise architecture; strong command of star schema design and canonical data model patterns.
• Azure Fabric & Microsoft stack — hands-on experience with Microsoft Fabric, Power BI semantic models, Azure Synapse, and associated consumption patterns.
• SQL and DAX — advanced proficiency for data transformation, metric calculation, and semantic layer development.
• LLMs and generative AI — practical experience designing data products and semantic 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.
• Stakeholder communication — able to articulate complex modelling and AI concepts to executive and non-technical audiences; comfortable working as a global lead with significant autonomy.
• 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 semantic modelling, with evidenced outcomes — including executive-adopted models, measurable efficiency savings, and reduced duplicated logic.
• Proven delivery of reusable semantic layers that enabled self-service reporting across multiple systems and global domains.
• Advanced SQL and DAX; strong command of dimensional modelling and star schema design.
• Experience with Microsoft Fabric, Power BI semantic models, 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.
• Experience with global data standardisation — harmonising definitions, taxonomies, and formats across regions.

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.
• Experience with analytics engineering frameworks such as dbt or equivalent.
• Relevant certifications in Microsoft Fabric, Power BI, Azure, AI/ML, or analytics engineering.

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 Azure/Fabric stack with active investment in AI tooling and a collaborative, growing data function.
• Competitive salary, benefits, and commitment to your professional development.

At RES, we celebrate difference. We encourage applicants with different backgrounds, ideas, and points of view — our multiple perspectives make us better at solving complex problems. We welcome applications regardless of ethnicity, culture, gender, nationality, age, sexual orientation, gender identity, disability, marital status, parental status, or social background.