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About
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.
Languages
- English
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