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Data EngineerMedReview Inc. • New York, New York, United States
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Data Engineer

MedReview Inc.
  • US
    New York, New York, United States
  • US
    New York, New York, United States
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À propos

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 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.
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  • New York, New York, United States

Compétences linguistiques

  • English
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