Description

You will design and implement a strategic data analytics roadmap for healthcare payer business operations, owning the full lifecycle of data engineering solutions.

Responsibilities

• Design and maintain scalable data pipelines using Python and PySpark to process large volumes of healthcare data in a multi-tenant platform.
• Implement ETL processes to extract, transform, and load data from diverse sources into target relational and file-based databases.
• Optimize data workflows for performance and scalability, addressing data volume, velocity, and variety.
• Develop monitoring and alerting mechanisms to identify data quality issues and performance bottlenecks.
• Model data structures to support processing requirements, leveraging deep knowledge of claims, enrollment, eligibility, and risk adjustment (HCC coding).

Required Skills

• 9+ years of professional experience, with at least 5 years focused on data engineering and pipeline optimization.
• Expertise in Python and PySpark for scalable data processing and analysis.
• Hands-on experience with AWS services, specifically S3, Glue, EMR, and Redshift.
• Proficiency in programming languages including Scala, Python, or R.
• Experience with big data technologies such as Hadoop and Spark.
• Strong knowledge of relational databases like PostgreSQL and MySQL.
• Practical experience with scientific computing libraries such as Numpy, Pandas, SciPy, PyTorch, or Pyarrow.
• Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.

Preferred Skills

• Relevant certifications in AWS or data engineering.

Key Skills

Education

Bachelor's or Master's degree in Computer Science, Data Engineering