Data Modeller / Analytics Engineer
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.
Job Summary
This is a rare opportunity to join a newly created global data modelling lead role, in a growing central data and analytics team. Your key work will be to lead the design and build of governed, reusable global data models that translate enterprise data into business-ready dimensions, facts and metrics for consistent reporting, self-service reporting, analytics and AI/ML readiness.
You will be the bridge between the data team, IT leaders and business leaders: understanding and defining requirements, shaping data products, modelling business logic, and enabling performant, well-documented accurate data delivery at a global scale. This work relates predominantly in year one to corporate services data, specifically finance and human resources.
You will be the delivery lead for semantic data modelling and analytics engineering. The focus is in Microsoft Azure Fabric, gold layer models, semantic models and AI-ready consumption.
This is not BI modelling, it is Microsoft Azure Semantic Data Modelling working in a data team with engineers to deliver complex global physical, logical and dimensional data models using the silver and gold layers with the latest modelling tools.
Accountabilities
Key accountabilities include but are not limited to:
- Design global semantic data models in Azure, aligned to agreed business definitions, KPIs and reporting departments in conjunction with executives, business domains and senior IT leaders.
- Develop and maintain metric definitions and calculation logic to ensure semantic model consistency across dashboards and reports.
- Build, deliver and maintain curated semantic data modelling and products with documentation, tests, and versioning.
- Partner with data governance, architecture, system owners, business domains and cyber to align your models to systems schemas, metadata management, business requirements, ownership, and certification/security.
- Optimise semantic azure data models for performance, quality and usability, ensuring scalable, future proof models are delivered.
- Collaborate with and delivery work with Data Engineers/Architects on upstream transformations and data quality rules, ensuring end-to-end traceability, lineage and master data management. Deliver semantic data models with report developers and end users of the data (business/IT/data practitioners) to make effective use of the model
- Be able to deliver semantic data modelling for AI/ML use cases by providing quality datasets and impactive data models and advise data scientists on engineering and modelling needs.
Skills
- Strong data modelling expertise: dimensional modelling, business rules, dimensions; data patterns.
- Ability to define and govern metrics and model consistency across multiple products and source system integrations.
- SQL mastery and experience with transformation frameworks and testing/documentation practices. SQL, building Star Schema data models and ETL & DAX.
- Deeply skilled in semantic azure data modelling, including semantic layers and performance/cost optimisation.
- Extensive skills in data quality, traceability and observability integrated into modelling workflows.
- Strong stakeholder skills to translate business requirements into robust data products.
- Effective communicator with strong influencing, negotiating, and relationship‑building skills. Ability to articulate modelling to executives. Ability to translate complex data into meaningful insight for non-technical audiences.
- Able to work independently, manage competing priorities, and lead through change.
- Provide hands-on technical guidance to delivery and data teams across data modelling as the global lead.
- Stay ahead of and implement global best practice in modern, scalable and future proof semantic data modelling including AI and automation.
Qualifications and Experience
- Bachelor’s degree in Data Analytics, Data Science, or a related field.
- Significant experience in analytics engineering, semantic modelling. Evidenced high quality, significant quantifiable outcomes from delivering data modelling. Providing high quality, consistent and highly maintained accurate views which are adopted by executives and used for ongoing decision making – with little re-work and high success rate for maintenance year on year – future proof data models.
- Deep understanding of semantic modelling patterns and how they fit into enterprise architecture, evidence through quantified outcomes of delivery.
- Proven delivery of reusable semantic layers that improved consistency and reduced duplicated logic across reports. Proven experience of delivering model that realise efficiency savings across global organisations through adoption of data from semantic models, reducing business domains teams manual work and efforts, enabling self-service reporting across multiple systems and domains.
- Knowledge and experience in employing global data standardization frameworks for harmonizing data definitions, taxonomies, and formats across regions.
- Experience in AI/ML enablement and integration with data and analytics platforms for modelling.
- Strong communication and stakeholder engagement skills, alongside technical breadth in data modelling and analytics engineering.
- Working knowledge and experience in of AI/ML and automation, as they apply to data modelling, reporting and analytics.
- Strong executive/senior stakeholder skills to translate business requirements into robust data products.
At RES we celebrate difference 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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