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 in‑office 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.

Basic Requirements:

• Degree or equivalent and typically requires 4+ years of relevant experience.
• 4+ years of experience in data engineering, analytics engineering, software development, computer science, or a related field, including experience delivering and supporting production data solutions.
• 4+ years of experience developing and optimizing data solutions in Databricks or Snowflake; Databricks preferred
• 4+ years of experience with Python and SQL for data pipeline, validation, automation, and application development
• Hands-on experience implementing data quality controls, monitoring, issue remediation, and reusable validation frameworks
• Experience with dimensional modeling, star schemas, database design, data warehousing, and consolidated enterprise data assets
• Ability to translate business rules into governed technical solutions and communicate complex concepts clearly to non-technical stakeholders
• Strong understanding of data governance, security, quality, lineage, retention, purge controls, and production support best practices

Preferred Skills/Experience:

• Must be comfortable and proficient working with large and/or complex databases

• Working knowledge with project management tools like Jira, Asana

• Strong knowledge of cloud platforms with Azure preferred

• Knowledge of data visualization tools such as Power BI, Tableau, or Looker.

Physical Requirements:

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