Data Analytics Engineer

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. This role offers location flexibility and is open to candidates across the United States. Candidates based in the Dallas-Fort Worth (DFW) area will be hired in a hybrid capacity and are expected to work onsite at our Las Colinas office a minimum of two (2) days per week, with the remaining days worked remotely. Specific inoffice days may be designated based on team needs and business priorities. About the Role: As a Data Analytics Engineer, you will design, implement, and own scalable data solutions for US Oncology datasets. The initial focus of this role is to reduce technical debt, strengthen data quality, establish sustainable data lifecycle management, and build consolidated dimensional datasets that support analytics and operational reporting. You will work extensively in Databricks and Python, integrate data through APIs and other source systems, and partner with technical and business stakeholders to deliver reliable, governed, reusable data assets. This role also includes data analysis, solution design, technical documentation, and direct analytical support to stakeholders. What You'll Do: - Data Quality Engineering: Design and implement reusable validations for completeness, accuracy, timeliness, consistency, and compliance with business rules; incorporate controls into regular pipeline monitoring and validation. - Technical Debt Reduction: Identify and resolve high-impact technical debt by standardizing engineering patterns, simplifying data flows, and improving the reliability, scalability, and supportability of existing solutions. - Data Lifecycle Management: Design, test, document, and implement a governed, auditable purge process that supports retention expectations, reduces unnecessary storage, and protects downstream dependencies. - Consolidated Data Assets: Design and build trusted, reusable datasets, including consolidated patient, doctor, and employee tables, using agreed business definitions, governance standards, and validation rules. - Dimensional Modeling: Develop and maintain scalable star-schema models and supporting technical specifications to improve query performance, simplify data consumption, and enable consistent metric definitions. - Data Integration and Platform Engineering: Build and optimize data pipelines within a medallion architecture using Databricks, Python, APIs, and cloud services, ensuring secure, reliable, and cost-efficient data flow across systems. - Documentation and Operational Readiness: Maintain data quality rules, lineage, technical designs, operating procedures, and support handoff materials to ensure long-term maintainability, compliance readiness, and effective production support. - Collaboration and Delivery: Partner with data engineers, analysts, business stakeholders, and internal or third-party teams to clarify requirements, align definitions, communicate risks, and deliver measurable improvements while supporting committed project and production needs. BasicRequirements: Degree or equivalent and typically requires 4+ years of r