Data engineer
Glasgow (Glasgow City)
Taylor Hopkinson
Data engineer
€55,000 a year
Posted: 1 June
The role
Data Engineer for a major offshore wind project in The United Kingdom
Responsibilities
- Design and implement scalable ingestion pipelines from multiple source systems including and internal business data sources. Ensure reliable, automated, and monitored data flows into the Bronze layer of the Medallion architecture.
- Work within client's existing security framework to establish compliant connectivity to operational data sources.
- Build and maintain Silver and Gold layer transformations in Databricks using Python and SQL.
- Onboard datasets into Unity Catalog, ensuring proper governance, lineage, and discoverability.
- Platform Collaboration & Delivery Support: support the ML/Data Scientist in preparing clean, structured datasets for anomaly detection and asset performance modelling. Contribute to technical documentation and ensure pipelines are maintainable and transferable.
- Stay current on Databricks and Azure platform developments relevant to the stack.
- Support the Digital & AI Strategy Manager in assessing feasibility of new data source integrations as the roadmap evolves.
Experience & Qualifications
- Master’s degree in Computer Science, Data Engineering, Software Engineering, or a related technical field.
- Professional certifications in Azure, Databricks preferred.
- Training or background in energy systems, renewable energy, offshore wind or BESS technologies is a strong plus.
- 4-7 years of hands‑on data engineering experience in a cloud environment.
- Demonstrated experience delivering production pipelines on Databricks and Azure (ADLS Gen2, ADF or equivalent).
- Proven ability to implement Medallion architecture or equivalent layered data modelling patterns.
- Experience with REST API ingestion and integration of business systems (ERP, finance tools).
- Experience in a contractor or project‑based delivery model preferred.
- Exposure to OT/SCADA environments or energy sector data.
- Exposure to MLOps workflows or collaboration with data science teams.
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