Director, Solutions Engineering

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:

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 and data science talent, set performance expectations, conduct regular assessments, and foster a collaborative and innovative work environment.

 Solutions Development:

Lead the design, development, and deployment of tailored solutions that address specific business needs and challenges. Collaborate with stakeholders to gather requirements, define solution scope, and develop project plans.

Data Product Delivery:

Oversee the design, development, and testing and deployment of data products for use in advanced analytics and data science 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.

Collaboration with IT and Cloud Strategy:

Work closely with IT to leverage Azure cloud capabilities, ensuring seamless integration of data products with existing IT infrastructure. Oversee the implementation of cloud-based solutions, ensuring optimal performance, security, and cost-efficiency. Stay informed on the latest Azure cloud technologies and recommend their adoption as beneficial.

Financial Management:

Track and manage the financial performance of the Data product delivery, including budgeting, forecasting, and financial reporting. Ensure projects are delivered within budget and provide regular financial updates to senior management. Identify cost-saving opportunities and optimize resource allocation to maximize ROI.

Process Optimization and Governance:

Establish and maintain processes for efficient data product development, including version control, quality assurance, and deployment strategies. Implement governance frameworks to ensure data security, compliance, and ethical use of data. Continuously evaluate and improve team workflows to enhance productivity and deliver high-quality outcomes.

Stakeholder Engagement:

Act as the primary point of contact for stakeholders across the organization, ensuring clear communication and alignment on data initiatives. Translate complex technical concepts into business-relevant insights and recommendations. Foster strong relationships with business units to understand their needs and drive data-driven decision-making.

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 12+ years of professional experience and 4+ 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.
• Leading large data/technical teams—Data Science Engineering, Solution Architects, and Data 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
• Deep expertise in data architecture, data modeling, and task estimations.
• Define and track key performance indicators (KPIs) to measure the data product delivery
• Practical hands-on experience with d