How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Senior Data and AI 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.
We're building a world-class global data platform and looking for a Senior Data and AI Engineer to help shape it. If you want to engineer things that matter — at scale, with the latest tooling — this is the role.
The Role
You'll be at the heart of RES's data platform — designing, building, and operating the pipelines, infrastructure, and datasets that power enterprise reporting, analytics, and AI/ML across the business.
This is a senior hands-on engineering role combining deep technical execution with architectural decision-making. You'll set engineering standards, drive automation and MLOps practice, and work across the full data stack — from ingestion through to feature-ready datasets that enable data scientists and AI teams to do their best work. You'll partner with architecture, governance, modelling, and analytics teams to deliver end-to-end data and AI engineering products, and mentor engineers around you.
What You'll Do
Data Platform Engineering
- Design, build, and operate reliable, secure, and observable data pipelines and curated datasets that power enterprise reporting, analytics, and AI/ML use cases.
- Own engineering quality, performance, and cost optimisation — implementing robust data quality controls, testing frameworks, monitoring, and observability across the platform.
- Build and maintain production-grade data infrastructure on Azure / Microsoft Fabric, including data lakes, lakehouses, and modern data warehouse patterns.
AI/ML Engineering
- Produce feature-ready datasets and optimised data products that enable data scientists, AI engineers, and analytics teams.
- Lead AI/ML engineering use cases — applying engineering best practice to model pipelines, data preparation, and AI-ready dataset design at scale.
- Evaluate and adopt emerging data and AI engineering tools and patterns; drive continuous improvement of RES's data ecosystem.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
MLOps & Automation
- Define and implement CI/CD pipelines for data engineering workflows; apply infrastructure-as-code and automated quality gates as standard practice.
- Lead engineering automation to reduce manual effort, improve reliability, and accelerate time-to-insight.
- Apply containerisation and orchestration tooling (e.g. Docker, Airflow, or equivalent) to production data workflows.
Technical Leadership
- Drive architectural decisions and shape the direction of the data platform.
- Partner across architecture, governance, data modelling, and reporting to deliver coherent, end-to-end data and AI products.
- Mentor and support engineers; set the standard for quality, craft, and engineering rigour across the team.
What You'll Bring
- Azure data platform — deep expertise across Azure Data Factory, Synapse, Microsoft Fabric, Purview, Unity Catalogue, and data lake / lakehouse architectures.
- Python — advanced proficiency including open-source data libraries, frameworks, and production pipeline development.
- SQL — expert-level for data modelling, transformation, and complex query optimisation.
- AI/ML engineering — experience building data infrastructure for machine learning and AI use cases, including feature engineering and model pipeline support.
- MLOps — CI/CD for data pipelines, infrastructure as code, containerisation (e.g. Docker), and orchestration tools such as Airflow or equivalent.
- Data quality & observability — hands-on experience with testing frameworks, monitoring, and quality controls in production environments.
- LLMs and generative AI — practical understanding of how to engineer data products and pipelines that support LLM and GenAI use cases.
- Technical leadership — track record of architectural decision-making, setting engineering standards, and mentoring engineers.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Your Background
Essential
- Degree in computer science, data engineering, software engineering, or a related field — or equivalent hands-on experience.
- Significant experience (typically 7+ years) delivering enterprise-grade data engineering solutions in production environments.
- Proven track record as a Senior Data Engineer, including building large-scale data systems using modern approaches and making architectural decisions.
- Deep expertise in the Microsoft Azure data ecosystem — ADF, Synapse, Fabric, Purview, Unity Catalogue.
- Advanced Python skills including open-source data libraries, frameworks, and messaging systems.
- Strong experience building and maintaining production data infrastructure for AI and ML consumption.
- Experience with MLOps practices: CI/CD for data pipelines, automated testing, and infrastructure as code.
Desirable
- Experience with modern data stack tooling — dbt, Airflow, Prefect, or equivalent orchestration and transformation frameworks.
- Exposure to working alongside data scientists and AI engineers in a shared platform model.
- Experience with automation tooling such as Power Automate, Power Platform, or equivalent.
- Relevant certifications in Microsoft Azure, data engineering, or AI/ML.
Why RES?
- Engineer at scale — a genuinely global data platform with real complexity and ambition behind it.
- A modern, cloud-first stack — Azure, Fabric, Synapse, and active investment in AI tooling.
- A collaborative, cross-functional data function with architecture, science, analytics, and engineering working closely together.
- Competitive salary, benefits, and commitment to your professional development.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills