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Azure data engineer (ref: 196126)

Chester
Forsyth Barnes
Azure data engineer
€60,000 a year
Posted: 6 April
Offer description

Type: Contract

Length: 6-month contract (strong potential to extend further)

Contact: Thomas.price@forsythbarnes.com


The Role

We’re looking for a skilled Azure Data Engineer to play a key role in driving a self-service BI transformation. This initiative is focused on enabling business users to independently create dashboards and insights—reducing reliance on Data Engineering teams and accelerating data-driven decision-making across the organisation.

This is an opportunity to shape how data is consumed at scale by building intuitive, governed, and scalable data platforms within the Azure ecosystem.


Key Responsibilities

* Design and build data pipelines and data models within the Azure ecosystem to support self-service analytics
* Enable and optimise a self-service BI environment, allowing business users to create their own dashboards and reports
* Collaborate with stakeholders to define data structures, semantic models, and governance standards
* Develop and maintain scalable, high-quality data models to support analytics use cases
* Integrate modern AI-assisted development tools (e.g. Claude Code, GitHub Copilot) into engineering workflows
* Ensure data is clean, reliable, and accessible for non-technical users
* Support best practices in version control, CI/CD, and collaborative development


Key Requirements

* Strong experience within the Azure data ecosystem (e.g. Azure Data Factory, Synapse, Data Lake, Fabric)
* Proven experience delivering or supporting self-service BI / analytics environments
* Solid understanding of semantic data modelling and enabling business-friendly data structures
* Experience using AI-assisted development tools such as Claude Code and GitHub Copilot
* Strong skills in SQL, data transformation, and data architecture
* Experience working with version control systems (e.g. GitHub)
* Ability to work closely with business stakeholders and translate requirements into scalable data solutions
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