Data Engineer - Databricks

Build and maintain scalable data pipelines that power McKesson’s commercial data products and analytics. You will develop reusable data assets, transform data using SQL and Databricks (preferred) or Snowflake, and ensure data quality and production reliability in collaboration with analytics and product teams.

• Design and develop data pipelines integrating proprietary and third-party data sources
• Build, optimize, and maintain data transformation pipelines for scalability and reliability
• Use SQL and/or Databricks or Snowflake to cleanse, standardize, and transform data
• Collaborate with Product, Analytics, and external-facing teams to deliver scalable data solutions
• Monitor and support data quality, pipeline performance, and reliability for production data assets

The role focuses on end-to-end data engineering: integrating proprietary and third-party sources, designing data pipelines, and building transformation workflows for structured and unstructured data. Core tools include SQL, Databricks (preferred), and Snowflake, with an emphasis on scalable, reliable, high-performance pipeline operations and data quality monitoring.

The ideal candidate is a mid-level Data Engineer with 4+ years of experience designing and optimizing scalable, reliable data pipelines. They are strong in SQL and have hands-on experience with Databricks (preferred) and/or Snowflake, including building transformation pipelines using structured and unstructured data. They can monitor data quality and pipeline performance in production and partner effectively with analytics and product teams to deliver reusable data assets.