RES
Data and AI Modeller / Analytics Engineer
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Data and AI Modeller / Analytics Engineer
Data and AI Modeller / Analytics Engineer
Make Power for Good
RES is the world's largest independent renewable energy company. Our mission is to 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
- AI consumption
- Document metric definitions, calculation rules, filters, and caveats so outputs can be safely used by both people and AI tools.
- Own:
- Version control
- Testing
- Documentation
- 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 certified, well-governed data rather than raw 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
- Certified semantic models
- Actively identify and resolve AI answer risks, including:
- Metric inconsistency
- Missing context
- Wrong filters
- Hallucinated definitions
- Unsupported conclusions
- Use LLMs and prompt engineering in your own workflow to accelerate:
- Model documentation
- Metric definition drafting
- Data lineage annotation
- Consistency checking across large model libraries
- Stay current with LLM tooling and agentic AI frameworks, shaping RES's semantic layer to be AI-ready.
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Machine Learning Enablement
- Produce feature-ready datasets and ML-optimised data products, reducing the data preparation burden and accelerating model development.
- Advise ML teams on data modelling requirements, including:
- Feature engineering considerations
- Training/validation data structure
- Label availability
- Implications of business rule logic on model inputs
- Design data products that support:
- Batch ML pipelines
- Real-time or near-real-time inference use cases
- Collaborate with data scientists to ensure:
- Model outputs and predictions are correctly integrated into the semantic layer
- ML-generated signals are available as governed, reusable metrics alongside traditional KPIs.
Stakeholder & Domain Collaboration
- Work with:
- Executives
- Business domain leads
- Senior IT stakeholders
- Understand reporting requirements and translate them into agreed, future-proof data models.
- Partner with:
- Data engineers
- Architects on upstream transformations
- Data quality, lineage, and master data management
- Collaborate with:
- Governance
- Architecture
- System owners, and cyber teams
- 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 equivalents — 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
- Scalability
What You'll Bring
Required Expertise:
- Semantic data modelling: deep expertise in physical, logical, and dimensional modelling within enterprise architecture; strong command of:
- Star schema design
- Canonical data model patterns
- Azure Fabric & Microsoft stack:
- Hands-on experience with Microsoft Fabric, Power BI semantic models, Azure Synapse, and 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
- ML enablement:
- Experience producing feature-ready datasets
- Understanding of ML pipeline data requirements
- Ability to collaborate effectively with data scientists and AI engineers
- KPI governance:
- Strong understanding of metric definition, business rules, and calculation logic across multiple systems
- Data quality & observability:
- Experience embedding data quality, lineage, and traceability into modelling workflows
- AI answer risk:
- Understanding of where LLMs can fail when consuming poorly governed data
- Designing models to mitigate AI risks
- Stakeholder communication:
- Ability to articulate complex concepts to non-technical audiences
- Comfort working as a global lead with autonomy
- Automation & scripting:
- Python, automation tooling (Power Platform or equivalent) for modelling workflows
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Essential Qualifications:
- Degree in:
- Data analytics
- Data science
- Computer science
- Related discipline — or equivalent experience
- Significant experience in:
- Analytics engineering
- Semantic modelling
- Evangelised outcomes including successful model adoption by executives, with evidence of:
- Measurable efficiency savings
- Reduced duplicated logic
- Proven delivery of:
- Reusable semantic layers
- Supported self-service reporting across multiple systems and global domains
- Advanced proficiency in:
- SQL, DAX
- Microsoft Fabric, Power BI semantic models, and Azure data platform experience
- Practical experience designing data products for LLM, generative AI or ML consumption, including:
- Understanding of structured data consumption by AI tools
- Identification of AI tool failure points
- Strong experience in working with senior stakeholders to define and govern metrics
- Experience with global data standardisation, including:
- Harmonising definitions across regions
- Integrating taxonomies and formats worldwide
Desirable Qualifications:
- Experience with RAG architectures and agentic AI frameworks in analytics contexts
- Hands-on experience with:
- Data scientists in ML feature engineering or model pipeline design
- Experience with analytics engineering frameworks like dbt or equivalents
- Relevant certifications in:
- Microsoft Fabric
- Power BI
- Azure
- AI/ML
- Analytics engineering
Why RES?
- Global remit: Modeling lead for a worldwide renewable energy business
- Work at the intersection of:
- Data
- Business Intelligence
- AI
- Your models directly determine the quality of every:
- AI output
- ML model
- Executive dashboard
- Modern Azure/Fabric stack, active investment in AI tooling and growing, collaborative data function
- Competitive salary, benefits, commitment to professional development
We celebrate diversity—applications from all backgrounds are encouraged. No discrimination on:
- Ethnicity
- Culture
- Gender
- Nationality
- Age
- Sexual orientation
- Gender identity
- Disability
- Marital status
- Parental status
- Social background
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