At a Glance
- Tasks: Design and build data pipelines and AI/ML solutions that power renewable energy insights.
- Company: Join RES, the world's largest independent renewable energy company.
- Benefits: Enjoy a competitive salary, great benefits, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career development.
- Why this job: Make a real impact on the future of zero-carbon energy with cutting-edge technology.
- Qualifications: 7+ years in data engineering, strong Azure and Python skills required.
The predicted salary is between 70000 - 90000 £ per year.
- 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 Gen AI 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.
- #J-18808-Ljbffr
Senior Data and AI Engineer in Gateshead employer: RES
At RES, we pride ourselves on being an excellent employer, offering a dynamic work culture that fosters collaboration and innovation in the renewable energy sector. As a Solar Asset Monitor, you will benefit from comprehensive training opportunities, a supportive team environment, and the chance to contribute to sustainable energy solutions while enjoying a flexible work schedule. Our commitment to diversity and employee growth makes RES a rewarding place to build your career in the thriving UK solar industry.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data and AI Engineer in Gateshead
✨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 Senior Data and AI 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 Senior Data and AI 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 Senior Data and AI Engineer in Gateshead
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.
