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 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.

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 s is a hands‑on technical role that sits at the intersection of data engineering, business intelligence, and artificial intelligence.

• 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.

• 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.

• 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.

• 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…