Remote 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.