Company: RES
Location: Kings Langley
Job Description:Description
Do you want to work to make Power for Good?We’re the world’s largest independent renewable energy company. We’re guided by a simple yet powerful vision: to create a future where everyone has access to affordable, zero carbon energy.We know that achieving our ambitions would be impossible without our people. Because we’re tackling some of the world’s toughest problems, we need the very best people to help us. They’re our most important asset so that’s why we continually invest in them.RES is a family with a diverse workforce, and we are dedicated to the personal professional growth of our people, no matter what stage of their career they’re at. We can promise you rewarding work which makes a real impact, the chance to learn from inspiring colleagues from across a growing, global network and opportunities to grow personally and professionally.Our competitive package offers a wide range of benefits and rewards.
Data and AI Modeller / Analytics Engineer
Job Summary
This is a rare opportunity to join a newly created global role within a growing central data and analytics team. The Data and AI Modeller / Analytics Engineer leads the design and build of governed, reusable global data models — translating enterprise data into business-ready dimensions, facts, and metrics for consistent reporting, self-service analytics, and AI/ML readiness.
The postholder acts as the bridge between the data team, IT, and business leaders: defining requirements, shaping data products, modelling business logic, and enabling performant, well-documented data delivery at global scale. This is a hands-on technical role working in Microsoft Azure Fabric across gold layer models, semantic models, and AI-ready consumption — ensuring business users and AI tools consume trusted definitions and governed metrics, not raw or uncontrolled data.
Key Accountabilities
Design global semantic data models in Azure aligned to agreed business definitions, KPIs, and reporting requirements, working with executives, business domains, and senior IT leaders.Build governed gold layer models, semantic models, and certified data products — including dimensional models, canonical models, business-ready views, and reusable semantic structures across enterprise domains.Translate business rules, reporting logic, and KPI definitions into trusted, reusable governed metric logic; develop and maintain calculation logic to ensure consistency across dashboards, reports, and AI-enabled tools.Design models that support dashboards, self-service analytics, and AI natural language querying; document metric definitions, calculation rules, assumptions, exclusions, filters, and caveats for safe consumption by users and AI tools.Own version control, testing, documentation, and governance of metric definitions and semantic models; identify and replace duplicate, conflicting, or ungoverned metrics with controlled enterprise definitions.Collaborate with data engineers and architects on upstream transformations, data quality, traceability, lineage, and master data management; partner with governance, architecture, system owners, and cyber to align models to metadata, ownership, and certification requirements.Support migration and rationalisation of existing Power BI datasets, measures, reports, and legacy reporting logic.Optimise Azure semantic models for performance, quality, and scalability; deliver modelling for AI/ML use cases and advise data scientists on engineering and modelling needs.Validate AI-generated analytical outputs against correct metric logic, filters, definitions, and the approved semantic layer; ensure AI tools consume only approved, access-controlled definitions and certified models.Skills and Competencies
Deep expertise in semantic Azure data modelling including physical, logical, and dimensional approaches within enterprise architecture.Advanced SQL, DAX, star schema design, ETL, analytics engineering, and Microsoft Fabric consumption patterns; experience with performance and cost optimisation.Strong understanding of KPI governance, metric definition, business rules, and calculation logic across multiple products and source systems.Extensive skills in data quality, traceability, and observability integrated into modelling workflows.Understanding of AI answer risks including inconsistent metrics, missing context, wrong filters, hallucinated definitions, and unsupported conclusions; working knowledge of AI/ML and automation as applied to data modelling and analytics.Effective communicator with strong influencing and stakeholder engagement skills; able to articulate complex modelling concepts to executive and non-technical audiences and work independently as the global modelling lead.Qualifications and Experience
Essential
Bachelor’s degree in data analytics, data science, or a related discipline.Significant experience in analytics engineering and semantic modelling with evidenced, quantifiable outcomes — including executive-adopted, future-proof models with high maintenance success rates and measurable efficiency savings.Proven delivery of reusable semantic layers that reduced duplicated logic and enabled self-service reporting across multiple systems and global domains.Experience with global data standardisation frameworks for harmonising definitions, taxonomies, and formats across regions.Experience in AI/ML enablement and integration with data and analytics platforms.Strong executive stakeholder engagement skills alongside technical breadth in data modelling and analytics engineering.Experience with AI-enabled analytics, natural language querying and governed data consumption.Relevant certifications in data modelling, analytics engineering, Microsoft, Power BI, SQL, Fabric and AI analytics.…
Posted: June 15th, 2026
