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Microsoft Fabric Data Engineer

Mace
Peterborough
1 month ago
Applications closed

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Microsoft Fabric Data Engineer

Microsoft Fabric Data Engineer

Microsoft Fabric Data Engineer

Microsoft Fabric Data Engineer

Microsoft Fabric Data Engineer

Microsoft Fabric Data Engineer

At Mace, our purpose is to redefine the boundaries of ambition. We believe in creating places that are responsible, bringing transformative impact to our people, communities, and societies across the globe. To learn more about our purpose, culture, and priorities, visit

Within our consult business we harness our unique combination of leading-edge practical expertise and project delivery consultancy to unlock the potential in every project.


Our values shape the way we consult and define the people we want to join us on our journey, they are:


Safety first- Going home safe and well.




Client focus-Deliver on our promise.




Integrity- Always do the right thing.




Create opportunity- for our people to excel.


The role is for a data engineer specializing in the Microsoft technology stack. It is a client-facing role supporting fee-earning projects and programmes.


The principal responsibility of the role is to contribute to the development of digital tools, processes and outputs, including the data infrastructure underpinning all BI activity, that support the wider project team and enable effective delivery of projects for our clients.


You’ll be responsible for:


You will be expected to work with Microsoft’s Fabric platform, principally within the Data Engineering and Data Warehousing workflows.




Identify the best method for extracting data from source systems on a case-by-case basis. Understand, and be able to draw on, effective use of tools such as Notebooks, Dataflow Gen2 and PowerShell.




Manage credentials, including service accounts and interfaces with Azure Key Vault, to ensure connections are made in the most secure way.




Build integrations with appropriate error handling and business logic to ensure there is no corruption, omission or duplication of ingested data.




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