Lead Data Engineer

Our client, a construction and project-based business, is building its enterprise data platform from the ground up, and they need a strong, hands-on backend data engineer to lead it. This is a genuinely greenfield opportunity: the architecture, pipelines, governance, and standards you put in place will define what good looks like at the data layer for years to come. The organisation isn't a massive, tech-heavy environment, so you won't be navigating layers of process or inherited technical debt. You'll be setting the technical direction yourself, working closely with the AI and product development team, while the data platform is built on Microsoft Fabric and the Azure data ecosystem by design. What You'll Be Doing Owning the design of the enterprise data architecture, including data domains, sources of truth, data models, and governed data flowsDesigning and implementing a scalable data platform on Microsoft FabricBuilding and maintaining reliable, production-grade data pipelines, applying DataOps practices such as version control, testing, and CI/CDDefining integration standards, patterns, and templates that govern how systems connect across the businessEstablishing the data governance framework, including data ownership, quality standards, and lifecycle managementBuilding the reporting and analytics foundations that support self-service analytics, predictive analytics, and machine learningPartnering with the AI team to ensure data foundations support agentic workflows, RAG pipelines, and AI-consumption-ready data structuresCommunicating data architecture and platform decisions clearly to non-technical stakeholders What you'll bring Proven senior-level data engineering experience, with the ability to design and build production-grade data pipelines, platforms, and architectureHands-on experience with Microsoft Fabric or the broader Azure data ecosystem (Azure Data Factory, Azure Synapse, Azure Databricks, or equivalent)Strong understanding of modern data architecture patternsExperience defining and implementing integration standards and data governance frameworksSolid DataOps practice, including version control, testing, and CI/CDA relevant tertiary qualification in IT, computer science, engineering, or a related disciplineActive, disciplined use of AI-assisted development and engineering tools in your day-to-day practice Nice to have Exposure to data tooling beyond the Microsoft ecosystem, such as Snowflake, BigQuery, or Apache SparkExperience working alongside AI or machine learning engineersExperience in construction, project-based, or field-intensive industries Why apply This is a rare chance to build a data platform end to end, with real ownership and executive backing, rather than maintaining someone else's legacy stack. If you want to shape what good looks like at the data layer for a business investing seriously in AI and data maturity, we'd love to hear from you.