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Strong employer brand, popular Data Analyst title, and mid-level (4+ years) requirement increase competition.
Core data platform skills are transferable, but healthcare risk-adjustment domain knowledge raises moderate specificity.
Explicit 4+ years requirement plus mandatory Databricks, ADF, Snowflake and cloud skills create strict filters.
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Architect and scale data ingestion pipelines using Azure-native technologies, Databricks, and Lakehouse architecture to support Risk Adjustment projects.
Ensure compliance and high performance of data platforms sourcing diagnosis data from claims and medical records.
Build robust data foundations to enable cross-functional analytics and improve predictive capabilities for Medicare population financial trends.
Bachelor's Degree in Computer Science Engineering.
Minimum 4 years of experience working with Cloud technologies including Databricks, Azure Data Factory (ADF), and Snowflake.
Good understanding of AI concepts and their implementation.
Work Experience Required: Minimum 4+ years as explicitly mentioned.
Experienced in architecting scalable data pipelines in a healthcare risk adjustment context leveraging cloud and big data technologies.
Familiar with Azure ecosystem and modern data engineering best practices focused on real-time and batch processing.
Capable of ensuring data compliance and optimizing data platforms to support predictive health analytics.