Data Engineer

Position Summary: MedReview Innovation and Development team is seeking a data engineer to function as the primary architect and operator of our data infrastructure. Your mission is to evolve our current environment into a rapid-acquisition engine capable of feeding real-time ML models, innovation, and operations while maintaining rigorous healthcare compliance standards. Responsibilities: Pipeline Architecture: Design, implement, and maintain end-to-end data pipelines on Azure , ensuring high availability and low latency for healthcare claim and analytics processing. High-Performance Storage: Manage and optimize ClickHouse as our primary analytical engine, focusing on rapid data ingestion and lightning-fast query performance for large-scale datasets. ML Data Readiness: Structure data environments to support the full ML lifecycle, from feature engineering and training to real-time model inference. MLOps Integration: Collaborate with Data Scientists to implement automated CI/CD pipelines for model deployment, monitoring, and retraining. Rapid Acquisition: Develop scalable frameworks to ingest diverse healthcare data sources (EDI, claims, clinical notes) with high velocity. Security & Compliance: Ensure all data structures and processes adhere to HITRUST/HIPAA standards, collaborating with IT and the leads for technical efforts for HITRUST certification readiness. Required Skills & Experience Cloud Expertise: 5+ years of experience in data engineering, with deep proficiency in Azure Data Factory, Azure Databricks, or Azure Synapse . OLAP Mastery: Proven experience managing and tuning ClickHouse (or similar columnar databases like Druid/Pinot) for massive datasets. Programming: Expert-level Python and SQL skills. ML Engineering: Familiarity with ML frameworks (PyTorch, TensorFlow) and MLOps tools (MLflow, Kubeflow, or Azure Machine Learning). Healthcare Domain: Prior experience with healthcare data formats (HL7, FHIR, 835/837) and a strong understanding of HITRUST/HIPAA security requirements. Scale-up Mindset: Ability to build "v1" processes while designing for 10x growth. Preferred Qualifications: Experience with Infrastructure as Code (Terraform, Bicep). Knowledge of stream processing (Kafka, Azure Event Hubs). Background in financial or payment integrity analytics.