Data Engineer 3

As a Data Engineer 3, you will design, develop, and maintain scalable data pipelines and analytics solutions supporting large-scale healthcare data processing. You will work extensively with CMS and Medicaid datasets, leveraging cloud-based technologies, particularly Databricks, to deliver reliable, secure, and high-performance data solutions. Using Python, PySpark, and SQL, you will build sophisticated ETL/ELT workflows and distributed data processing systems. You will play an important role in ensuring data quality, consistency, governance, and compliance with healthcare privacy requirements. Working closely with analytics, engineering, and DevOps teams, you will help strengthen data platform capabilities and support critical reporting and operational needs. This opportunity is ideal for an experienced data engineering professional interested in applying modern cloud technologies to meaningful healthcare data challenges.

The ideal candidate is an experienced data engineering professional with strong programming skills, cloud analytics expertise, and practical knowledge of healthcare data environments. The following qualifications and capabilities are required:

• Bachelor's degree with at least 5 years of relevant professional experience, or a Master's degree with at least 3 years of relevant experience.

• Previous experience working with CMS systems and/or Medicaid datasets.

• Hands-on experience with cloud-based data analytics platforms, particularly Databricks.

• Strong programming expertise in Python, PySpark, and SQL.

• Proven experience designing and developing distributed data processing pipelines.

• Experience handling, transforming, and analyzing large-scale healthcare datasets.

• Strong understanding of ETL/ELT architecture, development, and implementation.

• Ability to design scalable, reliable, and efficient data solutions.

• Strong analytical and problem-solving skills, with attention to data quality and technical accuracy.

• Effective communication and collaboration skills to work across analytics, engineering, and DevOps teams.

• U.S. work authorization is required.

• Ability to obtain and maintain a Public Trust clearance, which may include fingerprinting.

Additional qualifications considered an advantage include:

• Experience implementing data lake or lakehouse architectures.

• Familiarity with healthcare data interoperability standards.