RAAPID is a leading healthcare technology innovator specializing in AI-enabled risk adjustment solutions. Funded by Microsoft and Great Place to Work-certified organization, we serve payers, healthcare providers, and support organizations with cutting-edge technology solutions that optimize revenue, ensure compliance, and reduce administrative costs.
Why RAAPID?
Backed by Microsoft: Proudly funded by M12, Microsoft's venture fund, validating our technology innovation and market potential
Industry Pioneer: Leveraging state-of-the-art artificial intelligence, machine learning, vision AI, and knowledge graphs to transform healthcare operations
Strong Foundation: Built on four key pillars - Trust, Technical Competence, Stability, and Technical Innovation
Recognition: Proud recipient of HITRUST certification, demonstrating our commitment to security and compliance
Culture: Certified Great Place to Work, reflecting our dedication to employee satisfaction and professional growth
Mission-Driven: Focused on revolutionizing value-based healthcare through customizable, AI-powered solutions
Global Presence: Headquartered in Louisville, Kentucky, with a robust team of over 100 employees across the US and India
Role Summary
We are hiring a Senior Data Engineer to drive the development of scalable, high-performance data pipelines for a migration project to a modern Azure Native architecture. This role will be instrumental in building an AI-ready data platform using Microsoft Fabric (Fabric IQ) and enabling seamless integration with AI/ML workflows (Foundry IQ).
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Design and build robust, scalable data pipelines (batch + near real-time) using Microsoft Fabric (Data Pipelines, Lakehouse)
Lead migration of existing pipelines (AKS/custom ETL) to Fabric-native architecture
Implement Medallion Architecture (Bronze → Silver → Gold) for structured and unstructured healthcare data
Develop and optimize data models and transformations for analytics and AI use cases
Build ingestion frameworks for
APIs (FHIR, EHR systems)
Files (JSON, HL7, CSV)
Ensure data quality, validation (FHIR/claims), and observability across pipelines
Enable AI/ML workflows by preparing datasets for
RAG pipelines
Feature engineering and model consumption
Optimize pipeline performance, cost, and scalability
Collaborate with Architects, AI/ML engineers, and Product teams to translate requirements into data solutions
Mentor junior engineers and drive engineering best practices
Languages: Python, PySpark, SQL (advanced)
Azure / Fabric
Microsoft Fabric (preferred) OR Azure Data Factory, Synapse, Data Lake
Data Architecture
Lakehouse, Medallion Architecture
Batch + streaming data processing
Data Handling
JSON, Parquet, CSV, API integrations
Performance Optimization
Partitioning, indexing, query tuning
