We’re working with a leading organisation operating at the forefront of the energy sector, playing a critical role in supporting the transition to a more sustainable, low-carbon future.
As the business continues to invest in its data capabilities, they are looking for a Data Engineer to join their growing Data Platform team. This is an opportunity to work in a complex, evolving environment where data is central to decision-making across trading, operations, and commercial functions.
The Role
You’ll be responsible for building and maintaining scalable data pipelines that power analytics and reporting across the organisation. Working within a modern Databricks on AWS environment, you’ll help deliver reliable, high-quality data to support business insights.
Key Responsibilities
Build and maintain scalable ETL/ELT pipelines using Python and PySpark
Ingest and integrate data from multiple sources including trading, finance, and operational systems
Work within a Databricks Lakehouse architecture to transform and deliver data
Optimise Spark jobs for performance and cost efficiency
Implement and maintain data quality, governance, and reliability standards
Support the full data lifecycle from ingestion through to reporting and insight delivery
Collaborate with BI teams and stakeholders to ensure data is accessible and usable
Key Skills & Experience
Strong experience in Data Engineering (3–6 years)
Proficiency in Python and PySpark for large-scale data processing
Hands-on experience with Databricks (Delta Lake, Workflows, Lakehouse architecture)
Solid knowledge of AWS services (e.g. S3, Kinesis, Lambda, IAM)
Experience building production-grade data pipelines
Good understanding of data governance and data quality frameworks
Strong SQL skills and experience working with complex datasets
Desirable
Experience within energy, utilities, or similar complex industries
Exposure to CI/CD for data pipelines or modern data platform tooling