Senior Data Engineer

• $32m+ raised, backed by leading technology and industrial investors.
• Approximately 50 people, including world-class AI researchers and industrial specialists.
• Already deploying its technology with energy, refining and petrochemical customers.

You’ll join a small engineering team deploying this foundation model into industrial environments, building the data infrastructure that connects complex plant systems to production AI.

• Building production pipelines using Databricks, PySpark, Python and SQL.
• Integrating industrial data from SCADA, DCS, OPC servers and process historians.
• Transforming raw operational data into structured, AI-ready datasets.
• Supporting the move towards a Databricks Lakehouse/Lakebase architecture.
• Working directly with customers and ML engineers on real-world deployments.

• Fully remote, UK-based
• Occasional international customer visits, approximately quarterly

• Strong proficiency in Python for data processing, scripting, and pipeline orchestration.
• Hands‑on experience with AWS, Azure or GCP for secure and scalable data pipelines.
• Proven ability to work with Databricks and PySpark for large-scale distributed data processing
• Familiarity with time‑series or industrial data (control systems, DCS/SCADA logs, process historians).

Help build AI that makes the world's largest industrial facilities safer, cleaner and more efficient.

We're hiring for a well-funded British AI startup building a physics-informed foundation model for energy and industrial operations.

Refineries and petrochemical plants generate enormous amounts of data, yet much of it goes unused. This company have built a model that combines live operational data, physics and AI to help operators predict problems, optimise performance, reduce emissions and make better decisions.