Data Engineering Lead

Salary: £37,000 - 44,000 per year

Requirements:

• We require proven experience in a senior Data Engineering or Lead Data Engineer role.
• Were looking for strong hands-on experience with Microsoft Azure data technologies, including Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, and Azure Data Lake.
• We require advanced SQL development skills.
• Were looking for proficiency in Python and modern data pipeline orchestration tools, including monitoring and automation.
• We require a track record of building reliable, monitored, production-grade data pipelines.
• Were looking for experience integrating data from multiple business-critical applications and legacy systems.
• We require a good understanding of data security, data protection, and access control principles within cloud-based data platforms.
• Were looking for comfort working in regulated data environments with sensitive personal and litigation data.
• We require excellent stakeholder management and communication skills.
• We value exposure to predictive analytics, machine learning operationalisation, or AI platforms.
• We value experience with Dynamics CRM, case management platforms such as VisualFiles, and finance systems such as Elite 3E.
• We value an understanding of data modelling and data architecture best practices.
• We value experience leading or mentoring data engineering teams.
• Experience in insurance, legal services, claims management, or financial services is desirable but not mandatory.

Responsibilities:

• Well rely on you to design, develop, and maintain enterprise ETL/ELT pipelines that integrate data from case management, finance, CRM, and third-party systems into a central data platform.
• Youll lead the development and continuous improvement of our cloud-based data platform within Azure.
• Youll ensure our data pipelines are scalable, reliable, cost-effective, and resilient.
• Youll build and maintain datasets that support business reporting, analytics, dashboards, and data-enabled products.
• Youll establish and champion engineering best practices, including CI/CD, automated testing, version control, monitoring, and alerting on pipeline failures.
• Youll work with our Data Architects to implement target-state data models and modern data engineering principles.
• Youll partner with data scientists and analysts to operationalise advanced analytics and predictive models.
• Youll manage integrations with insurer portals and external data providers, ensuring high-quality, timely, and secure data flows.
• Youll support our ambition to become increasingly data-driven and AI-enabled.
• Youll help enable analytics, reporting, and AI initiatives through scalable data pipelines and architecture.

Technologies:

• AI
• Azure
• CI/CD
• Cloud
• CRM
• ETL
• Fabric
• Support
• Machine Learning
• Python
• SQL
• Security

More:

Were seeking an experienced Data Engineering Lead to shape and deliver our enterprise data platform, supporting advanced analytics, reporting, automated and AI-enabled processes, and data-powered digital products. Youll work with our Data Architecture, Analytics, and wider business teams, combining hands-on delivery with leadership and continuous improvement. This permanent role is fully remote in our CSG - Operations Hub (Data / Intelligence / Support) department. Youll help shape the future of data and AI in claims management, contribute to major transformation initiatives, influence strategic data decisions, and join a collaborative Data & Analytics function using Azure and AI technologies. We offer flexibility and work-life balance, a remuneration package including private medical and income protection insurance and discounted gym membership, professional development funding, social events, and opportunities to participate in ESG activities. Were committed to a diverse, inclusive, and authentic workplace guided by our principles of being Determined, Clear, Creative, and Supportive, and encourage applications even if experience doesnt match every qualification. Success will be reflected in reliable, high-performing pipelines and platform services; trusted data products; fewer data-related incidents and support issues; improved data access and adoption; and effective engineering practices and automation.

last updated 40 week of 2026