We're looking for a Finance Data Engineer who enjoys turning complex data into reliable products that people can trust. If you like working hands-on with Databricks, Python and SQL, translating Finance needs into scalable solutions, and connecting business requirements with modern data engineering, this one's for you.
What you will do:
- Design, build and operate modern data products and pipelines for the Finance organisation on Axpo's Databricks-based Group Data Platform.
- Develop, test and operate reusable data pipelines using Python, PySpark and SQL.
- Transform raw source data from ERP systems, business applications and other operational systems into curated, business-ready datasets for reporting, business intelligence, analytics and operational use cases.
- Apply appropriate patterns for incremental processing, historisation, reconciliation and auditability of Finance data.
- Implement data quality rules, validation checks and reconciliation controls to ensure completeness, consistency, accuracy, timeliness and traceability.
- Document datasets, data lineage, transformations,



business rules, assumptions and dependencies, ensuring reliable handover into operations.
- Work closely with Finance colleagues, product owners, IT stakeholders and platform teams to understand business requirements, source systems and data needs.
- Contribute to the evolution of the Finance data product portfolio and the modernisation of the broader Finance data landscape.

What you bring & who you are:
- A degree in Computer Science, Information Systems, Data Engineering or a related field. Additional education or practical training in Finance, Accounting, Treasury or a related discipline is an advantage.
- Experience in data engineering or a closely related technical role, with hands‐on experience using Databricks or a comparable modern data platform.
- Practical experience developing Python-based data engineering solutions, including PySpark data pipelines, good knowledge of SQL, dat

📌 Finance Data Engineer (Chamartín)
🏢 Axpo Group
📍 Chamartín