Senior Data Engineer/Software Developer

McKesson is an impact-driven, Fortune 10 company that focuses on making quality healthcare more accessible and affordable. They are seeking a Senior Data Engineer / Software Developer to support and scale AI-driven data platforms, focusing on building robust AI pipelines and engineering standards for advanced analytics outputs.

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
• 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

Requirements:

• Degree or equivalent and typically requires 7+ years of relevant experience
• A degree in a quantitative field such as Statistics, Machine Learning, Mathematics, Computer Science, Economics, Epidemiology or any other related field
• 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
• Master's Degree or higher preferred
• 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