Source description
About the role
About the Role We are looking for a Senior Databricks Data Engineer to Data Practice, focused on building scalable, high-performance data platforms using Databricks and modern cloud-native technologies. This is a data engineering role, centred on data pipeline development, distributed processing, and platform delivery, with limited DevOps or ML lifecycle responsibility. What You’ll Be Doing Designing, building, and operating batch and streaming data pipelines on Databricks Developing scalable data transformations using Spark / ELT patterns Engineering high-throughput data processing systems for large datasets Working with cloud-native data platforms (AWS preferred) Implementing data models, schema design, and optimisation techniques Ensuring data pipelines are reliable, performant, and production-ready Applying data quality, validation, and monitoring frameworks Collaborating with analysts, architects, and ML teams to enable downstream use cases Required Skills & Experience What You’ll Bring 5+ years’ experience in handson data engineering roles Strong programming skills in Python (and/or Java) and advanced SQL Proven experience building and running data pipelines in production Solid understanding of distributed systems and scalable data architectures Handson experience with AWS data services and cloudnative platforms Experience with Databricks or similar distributed data processing platforms Familiarity with CI/CD, monitoring, and operational support of data platforms Solid experience in data modelling and large-scale data system Nice to Have Exposure to ML / AI use cases via data platforms Exposure to modern BI and analytics tooling Experience working in regulated or securityconscious environments Consulting or clientfacing delivery experience