Data Engineer

The Role in a Nutshell

This role will provide dedicated data engineering capability within the Data Analytics & Intelligence team, with a primary focus on developing and optimising our use of Microsoft Fabric. The team has already begun establishing capability in Fabric alongside ongoing delivery. This role will build on that foundation—designing and implementing scalable data pipelines, reusable data models, and robust engineering patterns that support both current delivery and future growth.

You will play a key role in moving us from project-based builds towards a more structured, platform‑led approach to data.

Alongside platform development, you will also work across the whole business to improve how data is collected, stored, and managed. This includes reducing reliance on manual spreadsheets, supporting the evolution of our SQL‑based database in collaboration with the in‑house development team, and helping embed more consistent, well‑governed data practices across teams. Where new data requirements are identified, you will take ownership of how that data is ingested, structured, and stored—ensuring it is set up in a way that supports scalable, long‑term use across the business.

You will be central to strengthening our data foundations, enabling the BI and analytics teams to deliver faster, more reliable insight, and ensuring that data is structured in a way that is scalable, maintainable, and aligned with business needs.

• Design and implement robust, scalable data pipelines and models within Microsoft Fabric that support consistent ingestion, transformation, and storage of data from across the business.
• Leverage Fabric capabilities—including notebooks and pipelines—to enable efficient, repeatable, and automated data processing—reducing manual effort and improving reliability.
• Define and improve how data is collected, stored, and structured across the organisation, ensuring it is consistent, well‑organised, and accessible for reporting, analytics, and operational use.
• When new data requirements are identified, design and implement the appropriate ingestion pipelines, storage structures, and data models—ensuring new data is captured and integrated in a way that supports scalable, long‑term use.
• Support the transition away from spreadsheet‑driven workflows by delivering automated, governed data solutions that improve efficiency, accuracy, and consistency.
• Work with the in‑house development team to support and enhance the SQL‑based core database, ensuring it integrates effectively with the wider data platform.
• Strengthen data governance, quality, and consistency across the business by embedding good practices in how data is managed, transformed, and used.

• Microsoft Fabric experience is a must—building pipelines, managing storage, and structuring data.
• SQL and data modelling—working with relational data and shaping it for analysis.
• Data pipelines and integration—building pipelines across APIs, databases, and working with development teams.
• Power BI integration—structuring and optimising data for use within Power BI.
• Modern data architecture—understanding lakehouse / warehouse approaches.
• Python for data processing—transformation and automation.
• Data governance awareness—data quality, consistency, and best practices.
• Cross‑team working—improving data processes with technical teams and stakeholders.
• Energy / utilities experience (non‑essential)—familiarity with sector data and challenges.

• Ownership mindset—takes responsibility for delivering and maintaining high‑quality data solutions.
• Proactive—looks for ways to improve and simplify how data is handled.
• Collaborative—works effectively with BI developers, software developers, and business teams.
• Big‑picture thinking—understands how data fits across the wider business, not just one team/area.
• Structured approach—builds solutions that are scalable, maintainable, and well‑organised.
• Attention to detail—focused on data accuracy, consistency, and reliability.
• Curious and adaptable—open to learning and evolving with new tools and approaches.
• Pragmatic—balances ideal solutions with real‑world constraints and delivery needs.

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