Forward Deployed Data Engineer

The refineries and chemical plants that produce the fuel, plastics and fertiliser we rely on are often decades old. They're responsible for a significant share of global carbon emissions, and many still make critical decisions using less than 10% of the data their systems collect.

This company is using AI to change that.

They've built foundation models that combine physics, machine learning and industrial expertise to help some of the world's largest energy operators run their plants more efficiently.

It's already delivering results: tens of millions of dollars in operational improvements for customers, alongside lower emissions, safer operations and increased output.

Around 50 people, including scientists from Imperial College London and former Shell executives. Over $32m raised and growing.

You'll join a three-person engineering team deploying AI at industrial sites across Europe and Africa.

Working alongside an ML Engineer and Full Stack Engineer, you'll connect complex industrial data to the company's AI models, helping customers make better decisions using live operational data.

• Building pipelines to process live and historical data from sensors, control systems and industrial databases.
• Using Python, Databricks, PySpark and AWS to prepare large datasets for AI models.
• Designing and optimising PostgreSQL databases for high-frequency time-series data.
• Making sure data is accurate, consistent and traceable, even when sensors or data formats change.
• Keeping cloud and on-site systems synchronised to support model training and real-time predictions.

• Location: Fully remote, UK-based
• Travel: Approximately one trip per quarter to customer sites in Europe or Africa

• PostgreSQL: Strong experience with database design, indexing and query optimisation.
• Python: Confident building and maintaining production data pipelines.
• AWS: Hands-on experience with cloud infrastructure and data services.
• Databricks / PySpark: Experience processing large datasets at scale.
• Nice to have: Time-series or industrial sensor data, Kafka/Flink/Spark Streaming, or experience in energy, oil and gas.

You don't need an energy industry background, but you should enjoy solving complex engineering problems and working directly with customers.

This isn't a traditional reporting or data warehouse role. You'll be building the data infrastructure that makes production AI work in real industrial environments.