EU remote
Senior Data Engineer
About this role
We are looking for an experienced Data Engineer to join a dynamic data engineering team and contribute to the development of modern, scalable data solutions within a Microsoft Azure and Databricks environment. You will play a key role in designing, developing and maintaining data pipelines and data platforms, working with large and complex datasets to support business intelligence, analytics and data-driven decision-making.
This is an excellent opportunity for a Data Engineer who enjoys working with modern cloud technologies and is interested in data architecture, modelling and building robust data solutions. Key Responsibilities Design, develop and maintain scalable data pipelines and ETL/ELT processes using Azure Data Factory (ADF) and Azure Databricks . Develop efficient data processing and transformation solutions using Python and Databricks.
Work with large and complex datasets, ensuring data quality, reliability and performance. Contribute to the design and implementation of modern data architectures within the Azure ecosystem. Work with Medallion Architecture principles to build robust and scalable data solutions. Support the development of Data Vault and Business Vault architectures using Databricks on Azure. Work with Microsoft Fabric and OneLake , including the use of shortcuts to integrate and access data across different platforms.
Contribute to data modelling activities within Azure Data Warehouse and related analytical environments. Collaborate with Data Architects, Developers, Analysts and other stakeholders to understand requirements and translate them into effective technical solutions. Identify opportunities to improve data processing, architecture, performance and automation. Ensure solutions follow best practices around scalability, security, maintainability and data governance.
Must Requirements Experience creating data pipeline using python on Databricks Experience working at the curated and product layers of data engineering including transforming data into modelling technique including Data Vault 2.0, Kimball (dimensional) and 3NF A good understanding of techniques to manage schema evolution, slowly changing dimensions, data harmonisation. Practical experience of using DABs (Declarative Automation Bundles, nee Databricks Asset Bundles) for deployment Practical experience using git repositories and CI/CD pipelines (preferably with GitHub and ADO) Practical experience developing pipeline in a IDE environment (Preferably with VSCode) Nice to Have Experience with Microsoft Fabric and OneLake , particularly OneLake shortcuts.