Healthcare Analytics Data Engineer
Role summary
Magpie Health Analytics is seeking a Data Engineer to build and maintain cloud-native data solutions for healthcare clients. The role involves designing, developing, and optimizing scalable ETL/ELT pipelines using structured and semi-structured data. Responsibilities include refactoring legacy logic, developing complex healthcare claims logic with Python and SQL, implementing data quality checks, and utilizing cloud-native tools like Snowflake and Databricks. The engineer will troubleshoot performance issues, document data lineage, and stay current with emerging technologies to support data-driven decision-making and operational efficiency.
Company Description
Magpie Health Analytics empowers healthcare organizations with products and services that drive data-driven decisions. From improving risk adjustment performance to advancing value-based care and optimizing program outcomes, Magpie delivers actionable insights through advanced analytics. The organization is renowned for its expertise in healthcare data, risk adjustment, quality measures, and cutting-edge technologies such as SAS, Python, and Spark. Trusted advisors collaborate with clients to achieve their analytical and performance goals.
Role Description
We are seeking a Data Engineer to support the development of cloud-native data solutions that improve operational efficiency, support regulatory reporting, and drive actionable insight for our commercial and government healthcare clients. This role is responsible for building, maintaining, and optimizing scalable data pipelines and validation workflows across complex datasets—enabling downstream analytics, application logic, and system modernization initiatives.
Primary Duties & Responsibilities
- Design, develop, and maintain ETL/ELT pipelines using structured and semi-structured data from relational databases, flat files, APIs, and cloud data sources.
- Refactor legacy data validation logic into modern languages or open-source.
- Develop complex logic for healthcare claims using python and SQL.
- Develop and maintain efficient, testable, and reusable data processing scripts using Python, SQL, and cloud-native tools.
- Implement data quality and validation checks and document data lineage and pipeline logic for audit and reuse.
- Use cloud native tools (e.g. Snowflake, Databricks) to engineer data pipelines and workflows.
- Troubleshoot performance issues in data jobs and support data pipeline operations across environments
- Assist in developing or maintaining data documentation, including metadata, data dictionaries, and technical user guides.
- Stay up to date with emerging technologies, techniques, and trends to inform product development and decision-making.
Minimum Qualifications
- U.S. Citizen or Authorized to Work in the U.S. and have lived in the U.S three of the past five years.
- Must be able to obtain Public Trust Clearance.
- Bachelor's degree in computer science, engineering, statistics, or related field.
- 4+ years of experience in a data engineering, data pipeline development role.
- Strong proficiency in SAS and SQL, data transformation logic, and performance tuning for large datasets.
- Proficiency with Python and libraries such as Pandas, PySpark, or NumPy.
- Experience with modern version control (e.g. GitHub).
- Understanding of distributed computing solutions for data processing.
Preferred Qualifications
- Experience developing solutions in AWS cloud environments
- Experience with Snowflake or other cloud-native data warehouses.
- Experience supporting healthcare data systems or CMS data environments.
- Experience with application migration from legacy data logic (e.g., SAS) to modern data stacks.
- Experience with medical and pharmacy claims, member/beneficiary enrollment, and provider data.