Data Engineer – Microsoft Fabric & Delta Lake

Jellycat; Greater London; On-site; GBP 65,000 - 90,000; Full time

Job summary

Jellycat in London is seeking a hands-on Data Engineer to own defined areas of our data platform, from ingestion through to trusted, modelled data, with guidance as the business grows.

You will design and maintain scalable data pipelines in Microsoft Fabric, develop data models with Delta tables, and collaborate with analytics engineers, data analysts and data scientists to deliver reusable datasets and robust data quality.

Qualifications

• 2+ years’ proven experience as a Data Engineer or in a similar role.
• Proficiency in PySpark, Python and SQL for data pipelines.
• Experience with cloud data platforms (Microsoft Fabric, Azure Synapse or Databricks).
• Strong data warehousing and Lakehouse knowledge.
• Experience with Git and code reviews.

Responsibilities

• Design, build and maintain scalable data pipelines in Microsoft Fabric.
• Develop and maintain data models in OneLake using Delta tables, PySpark and SQL.
• Collaborate with analytics engineers, data analysts and data scientists to deliver datasets and ensure data quality.
• Implement data quality checks, monitoring and issue resolution.
• Follow data governance standards with documentation and access controls.
• Use Git and CI/CD for version control and deployment.
• Monitor pipeline performance and Fabric capacity usage.
• Contribute to team standards and knowledge sharing.

Skills

PySpark

Python

SQL

Git & CI/CD

Microsoft Fabric

Azure Synapse

Databricks

Tools

Microsoft Fabric

Azure Synapse

Azure Data Factory

Databricks

Job description

Jellycat in London is seeking a hands-on Data Engineer to own defined areas of our data platform, from ingestion through to trusted, modelled data, with guidance as the business grows.

You will design and maintain scalable data pipelines in Microsoft Fabric, develop data models with Delta tables, and collaborate with analytics engineers, data analysts and data scientists to deliver reusable datasets and robust data quality.