Senior Data and AI Engineer

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.