We are looking for a highly skilled Senior Data Engineer to design, build, and optimise large-scale data engineering pipelines that power data-driven decision-making across the organisation. 

In this role, you will work with modern technologies such as Databricks, PySpark, and Azure to process highly detailed data formats, ensure scalable ETL workflows, and deliver clean, structured, high-quality datasets. You will also mentor junior engineers, advocate best practices, and guide the team toward efficient, high-impact solutions. 

You’ll make an impact by: 

• Design and implement robust, scalable ETL pipelines for structured and unstructured data. 

• Use Databricks and PySpark to process and transform formats such as XML and Excel into usable data assets. 

• Define, document, and promote best practices across data engineering activities. 

• Build secure, scalable solutions using Azure services such as Data Factory and Data Lake. 

• Provide technical leadership, conduct code reviews, and support team members in delivering high-quality work. 

• Partner with analysts and business stakeholders to understand requirements and deliver effective data solutions. 

Your skills and experience: 

• Excellent understanding of ETL processes, data modelling, governance, and engineering principles. 

• Experience building large-scale data pipelines. 

• Experience with Databricks, SQL, PySpark, and Python. 

• Familiarity with Azure services (Data Lake, Data Factory); exposure to AWS or GCP is a plus. 

• Understanding of data lake and data warehouse architectures, including data security best practices. 

• Experience with CI/CD and workflow orchestration tools. 

• Excellent problem-solving and communication skills, with the ability to mentor others and collaborate across teams. 

• Experience within insurance or finance would be beneficial but not essential