Lead the team in designing and building 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.

5+ years of experience as a Data Engineer, Data Platform Engineer, or related role.
Proficiency in Python and 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 (bonus skills).
Experience working with EHR systems (Epic, Cerner, Meditech).

Data Platform Lead

Key skills