Senior Data Engineer/Software Developer

McKesson

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. We are seeking a Senior Data Engineer / Software Developer to support and scale our AI‑driven data platforms. This role focuses on building robust AI pipelines, shared tooling, and engineering standards that enable consistent, high‑quality generation and consumption of advanced analytics outputs across the organization. The engineer will play a key role in strengthening the technical foundation that supports analytics and oncology insights.

Requirements

Degree or equivalent and typically requires 7+ years of relevant experience.
3+ years of relevant experience in data engineering or software development roles supporting analytics or AI‑enabled solutions; healthcare experience preferred
Proficiency in Python and SQL, with demonstrated experience developing and maintaining reliable, production‑grade data pipelines and analytical datasets
Experience building and supporting internal tools or applications used for data validation, monitoring, review, or operational analytics workflows
Working knowledge of application integration patterns, including service‑based architectures and data access layers that support UI‑driven tools
Hands‑on experience using Databricks for data processing, analytics development, and collaboration with data science or analytics teams
Experience working within Microsoft Azure environments, applying standard engineering practices to deliver maintainable, well-documented solutions

Nice To Haves

Experience supporting AI or machine learning solutions in healthcare, oncology, genomics, or medical data domains is preferred but not required
Familiarity with machine learning or AI concepts, including model lifecycles, inference workflows, and integration of model outputs into analytics or data products
Exposure to Natural Language Processing or other unstructured data workflows, such as text ingestion, extraction, or downstream signal consumption
Experience with NoSQL or semi‑structured data stores and alternative data persistence patterns
Experience with analytics visualization tools or reporting solutions, and familiarity with modern scripting or web technologies used to support internal tools

Responsibilities

Collaborate with data scientists, machine learning engineers, and analytics teams to provide technical direction for AI and advanced analytics platforms
Work closely with data warehousing, data engineering, and cloud platform teams to design optimal architectures for AI‑driven data solutions
Enable the scalable use of AI‑generated outputs (e.g., ML predictions, extracted signals, model outputs) in conjunction with structured data to support analytics and oncology insights
Partner with senior management and stakeholders to communicate AI system capabilities, implementation approaches, assumptions, and limitations in clear, non‑technical language
Participate in the full lifecycle of AI and data platform solutions, including planning, design, implementation, deployment, monitoring, and ongoing maintenance
Design, build, and maintain production‑grade AI pipelines, shared frameworks, and supporting services in the cloud (e.g., AWS, GCP, Azure; Azure preferred)
Design, test, and maintain AI‑enabled applications and services using modern software engineering and testing methodologies
Perform code reviews and help define engineering and AI code standards to ensure high‑quality, scalable, and maintainable solutions
Develop and maintain scalable data and AI pipelines using Python and supporting technologies
Design and implement data architectures that support downstream analytics and access by McKesson analysts and AI data consumers
Drive innovation
Develop reusable engineering solutions to support AI workloads, model execution, inference pipelines, and integration into downstream data products
Evaluate new AI‑related tools, frameworks, and platforms to improve scalability, reliability, and developer productivity prior to broader adoption