Data Engineer With Using Apache Spark, Python, Scala.
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Summary
Contract role for a Data Engineer in Melbourne who designs, builds, and maintains scalable data pipelines and infrastructure using Apache Spark, Python, and Scala — including Kafka streaming, Docker/Kubernetes deployment, and testing with pytest, JUnit, and BDD tools.
Data Engineer with using Apache Spark, Python, Scala.
Location: Melbourne
Position Type: Contract
As a Data Engineer, you will be responsible for designing, developing, and maintaining robust data infrastructure and pipelines. You will work closely with cross-functional teams to understand business requirements, optimize data workflows, and ensure the reliability and performance of our data systems.
Key Responsibilities
- Design, build, and optimize data processing pipelines using Apache Spark, Python, Scala.
- Develop and maintain data ingestion and extraction processes, including streaming data pipelines using Kafka and batch processing workflows.
- Implement performance tuning techniques to optimize data processing and query performance, ensuring scalability and efficiency.
- Collaborate with DevOps teams to deploy and manage data infrastructure on NetApp S3 (very similar to AWS S3), Kubernetes.
- Containerize data applications using Docker and orchestrate deployment using Kubernetes for scalability and reliability.
- Develop and maintain unit tests using frameworks like pytest, junit, to ensure the quality and reliability of data pipelines.
- Implement and adhere to best practices for data governance, security, and compliance.
- Utilize Behavior-Driven Development (BDD) tools like Cucumber and Lettuce to write and execute test scenarios for dataworkflows.
- Stay up to date with emerging technologies and industry trends and evaluate their potential impact on our data infrastructure and processes.
Requirements
- Proven experience in designing and building scalable data pipelines using Apache Spark, Python, and Scala.
- Strong understanding of data warehousing concepts, ETL processes, and data modelling techniques.
- Experience with performance tuning and optimization of Spark jobs and SQL queries.
- Familiarity with stream processing frameworks like Kafka and messaging systems.
- Hands-on experience with containerization and orchestration tools like Docker and Kubernetes.
- Experience with unit testing frameworks like pytest, junit, and BDD tools like Cucumber and Lettuce.
- Excellent problem-solving skills and attention to detail.
- Strong communication and collaboration skills, with the ability to work effectively in a team environment