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Taylor Hopkinson Limited
London, City And County Of the City Of London, United Kingdom
Last month
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Work Location
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Education
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Posted
24 Apr 2026 (Last month)

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 clients 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 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

* 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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