Data Engineer
Summary
Data Engineer building scalable, reliable data pipeline components in Java (Spring Boot, REST APIs) within a ~100-person Data Intelligence department, working in multidisciplinary Agile/DevOps teams to deliver accurate, secure, timely data for operations, analytics and reporting. Stack includes Oracle/PostgreSQL, SQL, Kafka, Azure, Docker, Kubernetes and CI/CD tooling.
As a Data Engineer – Java, you will work within a Data Intelligence department of approximately 100 employees, operating across Agile/DevOps teams.
About the Role
You will be responsible for designing, developing and managing data solutions that are critical to the organisation’s information infrastructure. You will develop scalable and reliable data pipeline components using Java and ensure that data is available accurately, securely and on time for operational processes, analytics and reporting. You will work closely with Product Owners, Data Engineers, Testers, Business Analysts and other IT specialists within multidisciplinary Agile teams.
Responsibilities
- Designing, developing and managing data solutions that are critical to the organisation’s information infrastructure.
- Developing scalable and reliable data pipeline components using Java.
- Ensuring that data is available accurately, securely and on time for operational processes, analytics and reporting.
- Working closely with Product Owners, Data Engineers, Testers, Business Analysts and other IT specialists within multidisciplinary Agile teams.
Qualifications
- HBO or WO-level working and thinking capability.
- Background in IT/automation or demonstrable experience in a similar environment.
Required Skills
- Experience with Java development, preferably Java 17+.
- Experience with Spring Boot and REST API development.
- Knowledge of relational databases such as Oracle or PostgreSQL.
- Strong experience with SQL and data modelling.
- Experience with Git, Maven, Jenkins, Azure DevOps or comparable tools.
- Experience working with Agile/Scrum.
- Strong analytical and problem-solving skills.
- Ability to translate complex requirements into robust technical solutions.
- Experience with cloud platforms such as Azure.
- Knowledge of Kafka, streaming or event-driven architectures.
- Experience working in Data & Analytics environments.
- Experience with Docker, Kubernetes and CI/CD pipelines.