Description

You will lead the design and implementation of data analytics roadmaps and engineering pipelines within a healthcare payer context.

Responsibilities

• Design, develop, and maintain robust data pipelines using Python and PySpark to process large volumes of healthcare data in a multi-tenant platform.
• Execute complex data analysis projects focused on risk rating, claims adjudication, and enrollment optimization.
• Implement ETL processes to extract, transform, and load data from various sources into target databases.
• Optimize data workflows for performance and scalability, managing data ingestion, transformation, and storage in AWS.
• Develop monitoring and alerting mechanisms to proactively address data quality issues and performance bottlenecks.
• Collaborate with cross-functional teams to implement data models and ensure data integrity.

Required Skills

• Minimum 5 years of experience in healthcare payer analytics and data engineering.
• Expertise in Python programming and hands-on experience with PySpark for data processing.
• Proficiency with big data technologies including Hadoop, Spark, or Scala.
• Experience managing data in AWS services such as S3, Glue, EMR, and Redshift.
• Strong understanding of risk adjustment methodologies, including Hierarchical Condition Category (HCC) coding.
• In-depth knowledge of healthcare claims and enrollment data structures.
• Experience modeling data in relational databases like PostgreSQL and MySQL.
• Ability to translate technical findings to non-technical audiences.

Preferred Skills

• Experience working with large-scale multi-tenant analytics platforms.

Key Skills

Education

Any Gradute