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Mid-level, popular Data Engineer role with common tech stack (PySpark, Python, Kubernetes) increases applicant competition.
Core data engineering skills transfer broadly, but healthcare standards (DICOM/HL7) increase domain specificity.
Explicit 5–10 years requirement plus mandatory PySpark, Kubernetes, ETL and cloud skills enforce strict screening.
Design and develop cloud-native backend microservices to collect healthcare data from systems like HIS, RIS, and PACS.
Build and maintain ingestion pipelines for medical imaging and healthcare data in formats such as DICOM and HL7, ensuring data integrity and quality.
Develop scalable, fault-tolerant data transport services with monitoring and error-handling, deploy using containers and Kubernetes, and contribute to CI/CD automation.
5–10 years of experience in backend or cloud software development.
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
Strong proficiency with Python for backend microservices, PySpark for data processing, Kubernetes, ETL/ELT pipeline design, REST API development, and SQL.
Experience with healthcare data standards (HL7, DICOM) is nice to have but not mandatory.
Experienced in cloud-native microservices and scalable data ingestion pipelines with strong expertise in ETL/ELT orchestration tools (e.g., Airflow).
Familiar with healthcare data privacy regulations and secure handling of medical data in compliance with industry standards.
Operates effectively in cross-functional agile teams, collaborating with engineers and technical leaders to align integration solutions with platform architecture.