Responsibilites
- Design and build scalable data pipelines (batch & streaming) to process RTLS, IoT, and EHR data.
- Implement real-time event processing architectures using Kafka.
- Optimize ETL/ELT processes for efficient data ingestion, transformation, and storage.
- Develop and maintain data lakes and warehouses for analytics and machine learning applications - create scalable data layers—bronze, silver, and gold—that facilitate advanced analytics and machine learning applications.
- Partner with software engineers and cloud teams to maintain a highly available data platform.
- Work closely with third-party vendors (e.g., EPIC, Cerner, Meditech) to integrate, standardize, and secure data from EHRs
- Initiate and participate in architectural decisions to improve scalability, resiliency, and system efficiency.
- Ensure HIPAA and healthcare compliance standards are met in data storage and transmission.
- Optimize query performance, indexing, and partitioning for large-scale data processing.
- Implement role-based access control (RBAC), encryption, and anonymization for sensitive healthcare data.
What You Bring
- 5+ years of experience as a Data Engineer, Data Platform Engineer, or related role.
- Proficiency in Python, Scala, or Java for data processing and automation.
- Hands-on experience with big data frameworks like Apache Spark
- Experience with streaming and batch processing using Kafka
- Strong knowledge of cloud platforms (AWS) and their data processing services.
- Expertise in TimeScale
- Familiarity with containerization and orchestration tools (Docker, Kubernetes, Terraform)
- Previous experience in healthcare, hospital operations, or real-time systems.
- Experience working with EHR systems (Epic, Cerner, Meditech)

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