Director, Data Engineer

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.

Responsibilities:

Enterprise Data Engineering Delivery:

Oversee the design, development, and management of scalable and reliable data engineering solutions that support data products deployment for use in advanced analytics, data science, and AI initiatives. Ensure platforms are capable of handling complex data workflows and high-volume data processing. Guide the technical vision and strategy for an accurate, scalable, usable, and reliable data platform and infrastructure.

Service Leadership:

Manage a team of service leads, fostering a culture of collaboration, innovation, and excellence aligned to McKesson’s ICARE and ILEAD values. Provide mentorship and guidance to team members to ensure professional development and growth. Hire and retain top data engineering talent, set performance expectations, conduct regular assessments, and foster a collaborative and innovative work environment.

Strategic Leadership in Data Engineering:

Contribute to development of comprehensive enterprise data strategy that aligns with McKesson’s business objectives, emphasizing the strategic use of data as a key asset in driving business outcomes. Lead engineering practices and standards workstream.

Innovation in Data Technologies and Practices:

Lead the adoption of cutting-edge data technologies and methodologies to enhance data accessibility, quality, and insights. This also involves selecting appropriate technologies, tools, and platforms to ensure efficient data flow, data integration, and data governance. Lead PoC studies to bring and operationalize new features and capabilities.

Cross-Functional Collaboration and Integration:

Foster strong collaboration with business units, IT, and analytics teams to ensure data engineering practices meet evolving business needs. Develop and delivery data solutions backed by strong cross functional collaboration.

Governance, Compliance, and Data Security:

Implement engineering solutions to support robust data governance policies and practices to ensure data quality, compliance with global data protection regulations (e.g., GDPR, CCPA), and the security of sensitive and proprietary information. Ensure quality of enterprise data assets is maintained and enhanced by implementation of modern, automated processes.

Vendor and Stakeholder Management:

Manage relationships with technology vendors and partners to ensure the company has access to the best tools and services.

Minimum Requirements

Typically requires 13+ years of professional experience and 6+ years of diversified leadership, planning, communication, organization, and people motivation skills (or equivalent experience).

Critical Skills

• 12+ years of experience in a technology role; proven experience in a leadership role, preferably in a large, complex organization.
• 8+ years Data Engineering, Emerging Technology, and Platform Design experience
• 4+ years Leading large data / technical teams – Data Engineering, Solution Architects, and Business Intelligence Engineers, encouraging a culture of innovation, collaboration, and continuous improvement.
• Hands-on experience building and delivering Enterprise Data Solutions
• Extensive market knowledge and experience with cutting edge Data, Analytics, Data Science, ML and AI technologies
• Extensive professional experience with ETL, BI & Data Analytics
• Extensive professional experience with Big Data systems, data pipelines and data processing
• Deep expertise in Data Architecture, Data Modeling, and task estimations.
• Familiarity with data privacy standards, methodologies, and best practices
• Practical hands-on…