Data Engineer - Python | Pyspark | AWS | ETL/ELT Pipeline

Data Engineer - Python | PySpark | ETL/ELT

Key areas of responsibility

• Design and develop cloud-native backend microservices that collect data from medical systems, hospital information systems, radiology information systems, and PACS.
• Build ingestion pipelines for healthcare and imaging data, including DICOM and HL7 formats.
• Extend platform data-collection frameworks to support new data sources, integrations, and cloud-based workflows.
• Develop reliable, scalable, and fault-tolerant data-transport services with appropriate retry, error-handling, monitoring, and dead-letter mechanisms.
• Collaborate with data engineers to ensure ingested data meets defined schema, integrity, and quality standards.
• Package and deploy services using containers and Kubernetes.
• Contribute to automated build, testing, deployment, and CI/CD processes.
• Work with technical leaders and system engineers to align integration solutions with the wider platform architecture.
• Ensure services comply with cybersecurity, data-privacy, and regulatory requirements for healthcare and medical-device software.
• Develop unit, integration, and end-to-end tests and maintain clear documentation for APIs, data flows, and deployment configurations. Work Experience

 Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.   69 years of experience in backend or cloud software development.  Must Have:

• Python backend microservice development
• PySpark — data processing at scale
• Microservices — REST API design and inter-service communication
• Data modelling — understanding of data schemas and integration patterns
• Kubernetes — containerized service deployment and operations
• ETL/ELT – Pipeline design and orchestration (e.g., Airflow, Step Functions, or equivalent)
• SQL
• AWS

Nice to Have:

• Healthcare data standards: HL7, DICOM
• Clinical/IGT analytics platforms experience
• Streaming data technologies: Kafka, Kinesis, NATS

Experience implementing CI/CD pipelines and infrastructure-as-code. Familiarity with message brokers (e.g., Kafka, NATS, RabbitMQ) for event-driven data ingestion. Understanding of data privacy regulations and secure data handling practices in healthcare. Good collaboration skills in cross-functional agile teams. Proficiency in leveraging AI-assisted development tools to accelerate coding and testing.