Data Engineer - Databricks

McKesson • Columbus, OH

2d • Hybrid

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. The Data Engineer - Databricks with CoverMyMeds will support and expand the data platforms that power our commercial data products and analytics offerings, with a focus on building and maintaining scalable data pipelines. This role will contribute to the design and delivery of reliable, reusable data assets that enable both internal teams and external partners to derive value from our data. You will work across proprietary and third-party data sources to build well-structured, high-quality datasets and pipelines that support commercialization efforts. This role partners closely with Data Systems Analysts, Product, and Analytics teams to translate evolving business concepts into scalable, production-ready data solutions. Candidate must reside in the Columbus, OH area to support a hybrid work schedule. At this time, we are not able to offer sponsorship for employment visas for this role. This includes individuals currently on F-1 OPT, STEM OPT, or any other visa status that would require future sponsorship. Candidates must be authorized to work in the United States on a permanent basis without the need for current or future sponsorship.

Requirements

• Bachelor’s degree in Computer Science, Information Systems or related field, or equivalent experience, and typically requires 4+ years of experience in data engineering, analytics engineering, or modern data platform environments
• Hands-on experience building and maintaining data pipelines in cloud environments (Databricks, Snowflake, or similar)
• Strong SQL skills and experience transforming data for analytical, reporting, or product-oriented use cases
• Experience integrating data from multiple internal and third-party systems
• Experience working with structured and semi-structured data in batch and/or streaming environments
• Working knowledge of data modeling principles and data quality practices
• Experience supporting analytics, reporting, or externally facing data use cases

Nice To Haves

• Experience with Databricks (Delta Lake, Spark, workflows, or pipeline orchestration)
• Experience with or interest in data commercialization, data products, or externally facing analytics solutions
• Experience building production-ready data pipelines in iterative environments
• Comfort working with evolving requirements and ambiguity
• Ability to translate loosely defined business ideas into structured data outputs
• Strong collaboration skills across product, analytics, and technical teams
• Ownership mindset with a bias toward execution

Responsibilities

• Design and develop data pipelines that integrate proprietary and third-party data sources to support commercial data products and proof-of-concept initiatives
• Build, optimize, and maintain data transformation pipelines with a focus on scalability, reliability, and performance
• Work with structured and unstructured data to prepare enhanced datasets for internal stakeholders and external use cases
• Write SQL and/or use cloud-based tools such as Databricks (preferred) or Snowflake to cleanse, standardize, and transform data aligned to business use cases
• Collaborate with Product, Analytics, and external-facing teams to translate commercialization objectives into scalable data solutions
• Contribute to data models and reusable pipeline patterns that support future data product expansion
• Partner with application and platform teams to understand upstream data flows and implement efficient pipeline solutions
• Monitor and support data quality, pipeline performance, and reliability for production data assets

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees