Senior Data Engineer – Software Developer

Role Overview

• 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 and 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
• 3+ years of relevant experience in data engineering or software development roles supporting analytics or AI-enabled solutions
• Proficiency in Python and SQL
• 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

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Python
• SQL

Benefits

• competitive compensation package
• annual bonus or long-term incentive opportunities