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# Data Analytics Engineer

- **Company:** McKesson
- **Location:** Alpharetta, GA, United States
- **Experience:** Experienced
- **Salary:** $111,800.0 - $186,400.0
- **Contract:** Permanent contract
- **Skills:** Query Performance, Adaptable Database Systems, Application Programming Interfaces (APIs), Data Analysis, Asana, JIRA, Microsoft Azure, Information Engineering, Data Governance, Data Integration, Data Systems, Data Visualization, Data Warehousing, Database Design, Digital Assets, Dimensional Modeling, Information Lifecycle Management, Python (Programming Language), Operational Databases, Power BI, Cloud Services, Software Engineering, SQL Databases, Data Streaming, Tableau (Software), Snowflake, Technical Debt, Build Management, Information Technology, Data Analytics, Looker Analytics, Data Pipelines, Databricks
- **Published:** October 20, 2026
- **Apply:** https://www.dice.com/job-detail/c5d72d99-903d-42e1-b7da-5645f62139ae

## About the Role

* 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., Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future.

## Description

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.