Overview We’re looking for a skilled Data Analytics Engineer to help drive the evolution of our clients data platform. This role is ideal for someone who thrives on building scalable data solutions and is confident working with modern tools such as Azure Databricks, Apache Kafka, and Spark. In this role, you’ll play a key part in designing, delivering, and optimizing datapipelines and architectures. Your focus will be on enabling robust data ingestion and transformation to support both operational and analytical use cases. If you’re passionate about data engineering and want to make a meaningful impact in a collaborative, fast-paced environment, we want to hear from you! Role and Responsibilities

Designing and building scalable datapipelines using Apache Spark in Azure Databricks Developing real-time and batch data ingestion workflows, ideally using Apache Kafka Collaborating with data scientists, analysts, and business stakeholders to build high-quality data products Supporting the deployment and productionisation of machine learning pipelines Contributing to the ongoing development of a Lakehouse architecture Working in an Agile/DevOps environment to continuously improve platform performance and reliability

Essential Skills and Experience We’re seeking candidates who bring strong technical skills and a hands-on approach to modern data engineering. You should have:

Proven experience with Azure Databricks and Apache Spark Working knowledge of Apache Kafka and real-time data streaming Strong proficiency in SQL and Python Familiarity with Azure Data Services and CI/CD pipelines in a DevOps environment Solid understanding of data modelling techniques (e.g., Star Schema) Excellent problem-solving skills and a high attention to detail Experience working with unstructured data sources (e.g., voice) Exposure to Power BI for downstream reporting (desirable, but secondary to platform engineering skills) Previous experience in regulated industries

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