How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Senior Data and AI Engineer
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 are 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 core 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 practices, and work across the full data stack.
- Partner with architecture, governance, modelling, and analytics teams to deliver end-to-end data and AI engineering products.
- Mentor engineers around you and set the standard for quality, craft, and engineering rigour.
Responsibilities
Data Platform Engineering
-
Design, build, and operate reliable, secure, and observable data pipelines and curated datasets that empower 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
- Modern data warehouse patterns
AI/ML Engineering
-
Produce feature-ready datasets and optimised data products for data scientists, AI engineers, and analytics teams.
-
Lead AI/ML engineering use cases, applying best practices to:
- Model pipelines
- Data preparation
- AI-ready dataset design at scale
-
Evaluate and adopt emerging data and AI engineering tools and patterns, driving 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, standardising infrastructure-as-code and automated quality gates.
- Lead engineering automation to:
- Reduce manual effort
- Improve reliability, and accelerate time-to-insight.
- Apply containerisation (e.g. Docker) and orchestration tooling (e.g. Airflow, or equivalent equivalents) to production data workflows.
Technical Leadership
- Drive architectural decisions and shape the direction of the data platform.
- Partner with cross-functional teams in architecture, governance, data modelling, and reporting, delivering coherent end-to-end products.
- Mentor and guide engineers in crafting data solutions with high standards for quality, reliability, and scalability.
Requirements
What You'll Bring
Core Technical Skills
- Azure data platform: Deep expertise across:
- Azure Data Factory
- Synapse
- Microsoft Fabric
- Purview
- Unity Catalogue
- Data lake & lakehouse architectures
- Python: Advanced proficiency, including:
- Open-source data libraries
- Frameworks
- Production pipeline development
- SQL: Expert-level for:
- Data modelling
- Transformation
- Complex query optimisation
- AI/ML engineering: Experience building data infrastructure for:
- Machine learning
- AI use cases
- Feature engineering
- Model pipeline support
- MLOps:
- CI/CD for data pipelines
- Infrastructure as code
- Containerisation (Docker, equivalents)
- Orchestration tools (e.g. Airflow)
- Data quality & observability: Hands-on experience with:
- Testing frameworks
- Monitoring
- Quality controls in production environments
- LLMs and generative AI: Practical knowledge of engineering data products and pipelines for LLMs and GenAI applications.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Leadership Experience
- Track record of architectural decision-making
- Ability to set engineering standards
- Mentoring experience for technical teams.
Background
Essential
- Degree in computer science, data engineering, software engineering, or related field — or equivalent experience.
- Significant experience (typically 7+ years) designing and building enterprise-grade data systems in production environments.
- Formal seniority as a Senior Data Engineer, including:
- Constructing large-scale data systems with modern approaches
- Architectural decisions
- Deep expertise in the Microsoft Azure ecosystem, including:
- ADF
- Synapse
- Fabric
- Purview
- Unity Catalogue
- Advanced Python proficiency:
- Experience with open-source data libraries and production pipeline development.
- Production-grade data system design for AI and ML consumption.
- Experience with MLOps practices, including:
- CI/CD for data pipelines
- Automated testing
- Infrastructure as code
Desirable
- Knowledge of modern data stack tooling, such as:
- dbt
- Airflow
- Prefect
- Equivalent orchestration and transformation frameworks
- Collaboration experience with data scientists and AI engineers in a unified platform model.
- Familiarity with automation tooling, such as:
- Power Automate
- Power Platform
- Equivalent systems
- Relevant certifications, including:
- Microsoft Azure
- Data engineering
- AI/ML disciplines
Why RES?
- Build world-changing systems at global scale — drive meaningful ambition with complex, real-world challenges.
- Modern, cloud-first tech stack:
- Azure
- Microsoft Fabric
- Hybrid data warehousing solutions
- Collaborative team of experts in architecture, science, analytics, and engineering united by engineering excellence.
- Competitive remuneration, benefits, and commitment to professional growth.
“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