Data Engineer | Energy & Commodities Trading | Greenfield Data Transformation | £85,000 + Bonus & Benefits | Hybrid London
Data Engineer required for a global Energy & Commodities Trading organisation, supporting a greenfield data transformation programme within a fast-growing and highly commercial trading environment. This is an exciting opportunity to join a core engineering team responsible for shaping and delivering a scalable, enterprise-grade data platform. You will play a key role in building modern data capabilities that support trading, PnL analysis, market data processing, and strategic investment decisions. Working within a small but highly skilled team, this is a hands‑on role with significant influence over architecture, tooling, and engineering standards.
The Opportunity
- Design, develop, and maintain scalable cloud‑based data platforms
- Build and optimise data pipelines for batch and real‑time data processing
- Support the adoption and implementation of Azure Databricks across the organisation
- Process and integrate market and trading data to support analytics and reporting
- Develop robust data models aligned to trading and operational workflows
- Ensure performance optimisation, data lineage, auditability, and governance controls
- Partner with the Head of Core Engineering and wider technology teams to align data strategy with business growth objectives
- Integrate data platforms with front‑end BI tools such as Power BI and Tableau
This role forms part of a broader Azure‑focused transformation, assessing key platform features and implementing scalable, production‑ready solutions.
Technology Environment
You will be working across a modern Azure data stack, including:
- Synapse / Fabric
- Snowflake
- Python & PySpark
- SQL
- DevOps & CI/CD tooling within Azure
Experience Required
- Strong experience in modern data engineering within trading or financial services (energy/commodities experience highly desirable)
- Deep hands‑on experience with Azure Databricks
- Strong Python programming capability (including PySpark)
- Advanced SQL skills
- Experience building and orchestrating scalable data pipelines
- Strong understanding of data modelling and architecture principles
- Familiarity integrating data platforms with Power BI and/or Tableau
- Appreciation for data governance, auditability, and regulatory considerations
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