Data Analytics Engineer

McKesson•Irving, TX

3d•Hybrid

About The Position

As a Data Analytics Engineer, you will design, implement, and own scalable data solutions for US Oncology datasets. The role initially focuses on reducing technical debt, strengthening data quality, establishing sustainable data lifecycle management, and building consolidated dimensional datasets for analytics and operational reporting. You will work extensively with 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. The role also includes data analysis, solution design, technical documentation, and direct analytical support to stakeholders. The position offers location flexibility across the United States. Candidates based in the Dallas–Fort Worth area will work in a hybrid capacity, onsite at the Las Colinas office at least two days per week, with remaining days remote. Candidates within 50 miles of Irving, Texas, are required to be onsite two days per week. Up to 10% nationwide travel is required, averaging two to three times per year.

Requirements

A degree or equivalent and typically 4+ years of relevant experience.
At least 4 years of experience in data engineering, analytics engineering, software development, computer science, or a related field, including delivering and supporting production data solutions.
At least 4 years of experience developing and optimizing data solutions in Databricks or Snowflake; Databricks is preferred.
At least 4 years of experience with Python and SQL for data pipelines, 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.
Must be comfortable and proficient working with large and/or complex databases.
Must be currently authorized to work in the United States on a full-time basis without employer immigration support or sponsorship now or in the future.
Candidates within a 50-mile radius of Irving, Texas, must work onsite two days per week.
Able to travel nationwide up to 10%, averaging two to three times per year.

Nice To Haves

Working knowledge of project management tools such as Jira or Asana.
Strong knowledge of cloud platforms, with Azure preferred.
Knowledge of data visualization tools such as Power BI, Tableau, or Looker.

Responsibilities

Design and implement reusable data-quality validations for completeness, accuracy, timeliness, consistency, and compliance with business rules; incorporate controls into pipeline monitoring and validation.
Identify and resolve high-impact technical debt by standardizing engineering patterns, simplifying data flows, and improving solution reliability, scalability, and supportability.
Design, test, document, and implement a governed, auditable data-purge process that supports retention expectations, reduces unnecessary storage, and protects downstream dependencies.
Build trusted, reusable consolidated patient, doctor, and employee datasets using agreed business definitions, governance standards, and validation rules.
Develop and maintain scalable star-schema models and technical specifications to improve query performance, simplify data consumption, and enable consistent metric definitions.
Build and optimize data pipelines within a medallion architecture using Databricks, Python, APIs, and cloud services, ensuring secure, reliable, and cost-efficient data flow.
Maintain data-quality rules, lineage, technical designs, operating procedures, and support handoff materials for maintainability, compliance readiness, and production support.
Partner with data engineers, analysts, business stakeholders, and internal or third-party teams to clarify requirements, align definitions, communicate risks, and deliver improvements while supporting project and production needs.
Provide data analysis, solution design, and direct analytical support to stakeholders.