Description

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.

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.

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.