Intermediate Data Engineer
Summary
Build and maintain scalable data pipelines using Databricks, Apache Spark, and Azure, automating workflows and CI/CD to deliver reliable, high-performance data solutions.
Key Responsibilities
Data Engineering & Platform Development
- Design, build, and maintain scalable data pipelines and workflows.
- Develop and optimise data processing solutions using Databricks and Apache Spark.
- Build and support end-to-end data integration and transformation processes.
- Ensure data solutions are reliable, efficient, and aligned with best-practice engineering standards.
Cloud & Big Data Solutions
- Develop solutions within Azure cloud environments.
- Support the implementation and optimisation of modern data architectures.
- Work with large-scale datasets and distributed computing frameworks.
- Contribute to the ongoing evolution of the organisation's data platform capabilities.
Data Pipeline Automation & CI/CD
- Develop and maintain CI/CD pipelines to support efficient deployment and release processes.
- Implement automation strategies that improve data delivery, quality, and operational efficiency.
- Support monitoring, testing, and optimisation of data engineering workflows.
Collaboration & Solution Delivery
- Work closely with Data Engineers, Developers, Analysts, and Business Stakeholders to deliver high-quality data solutions.
- Participate in solution design discussions and technical planning.
- Contribute to the continuous improvement of data engineering practices and standards.
Innovation & Continuous Learning
- Stay current with emerging technologies and trends within data engineering and cloud platforms.
- Identify opportunities to improve performance, scalability, and maintainability across data solutions.
- Contribute to knowledge sharing and technical excellence within the team.
Requirements
Essential Skills & Experience
- 3-5 years' experience in a Data Engineering, Backend Development, or similar technical role.
- Previous experience working as a Data Engineer.
- Strong experience with Databricks.
- Proven expertise in Apache Spark and cluster computing environments.
- Advanced Python skills, including mandatory PySpark experience.
- Strong SQL development skills.
- Experience developing and maintaining CI/CD pipelines.
- Experience building scalable and reusable data pipelines.
- Solid software engineering and backend development foundations.
- Exposure to Azure cloud services and infrastructure.
Advantageous Experience
- Microsoft Fabric experience.
- Medallion Architecture experience.
- Experience working with modern data warehousing and analytics environments.
- Exposure to cloud-native data engineering practices.
What Success Looks Like
- High-performing and scalable data pipelines are delivered and maintained.
- Data workflows are optimised for reliability, performance, and cost efficiency.
- CI/CD processes support efficient and consistent deployments.
- Strong collaboration with technical and business teams results in high-quality data solutions.
- Best practices for data engineering, cloud development, and automation are continuously adopted and improved.
Apply now!