At a Glance
- Tasks: Design and build global data models for impactful analytics and AI readiness.
- Company: Join the world's largest independent renewable energy company focused on zero carbon energy.
- Benefits: Enjoy a competitive package with diverse benefits and opportunities for personal growth.
- Other info: Collaborative environment with a focus on professional development and global teamwork.
- Why this job: Make a real impact in renewable energy while working with cutting-edge data technologies.
- Qualifications: Bachelor's degree in data analytics or related field; experience in analytics engineering required.
The predicted salary is between 60000 - 80000 £ per year.
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.
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
- 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.
Data and AI Modeller / Analytics Engineer employer: RES
At RES, we are committed to fostering a dynamic and inclusive work environment where every employee can thrive. As the world's largest independent renewable energy company, we offer our Data and AI Modeller / Analytics Engineer the chance to engage in meaningful work that contributes to a sustainable future, alongside a diverse team of experts. With a strong focus on personal and professional development, competitive benefits, and the opportunity to work with cutting-edge technology in a global setting, RES is an exceptional employer for those looking to make a real impact in the renewable energy sector.
StudySmarter Expert Advice🤫
We think this is how you could land Data and AI Modeller / Analytics Engineer
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like RES!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Data and AI Modeller / Analytics Engineer at RES.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like RES.
✨Apply Directly through Our Website
When you find a suitable opening like Data and AI Modeller / Analytics Engineer at RES, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Data and AI Modeller / Analytics Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at RES, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at RES. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at RES
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at RES!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
