Manager, Data Engineering – Research, Oncology & AI Value Realization

Senior Manager, Data Engineering

Penn Medicine is dedicated to our tripartite mission of providing the highest level of care to patients, conducting innovative research, and educating future leaders in the field of medicine. Working for this leading academic medical center means collaboration with top clinical, technical and business professionals across all disciplines. Today at Penn Medicine, someone will make a breakthrough. Someone will heal a heart, deliver hopeful news, and give comfort and reassurance.

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This Senior Manager, Data Engineering will lead a team of Data Engineers and Data Architects responsible for delivering the foundational data products, platforms, and engineering capabilities that support Penn Medicine's strategic priorities across Research, Oncology, and AI-enabled transformation. The role will be accountable for establishing a scalable and trusted data ecosystem that enables advanced analytics, operational decision-making, clinical innovation, and scientific discovery.

Working closely with clinical, research, operational, informatics, and analytics leaders, this individual will oversee the design, development, and governance of enterprise data assets that support complex research initiatives, oncology programs, and the measurement of AI-driven interventions. This includes enabling access to high-quality, curated data; accelerating the development of analytics and AI solutions; and ensuring robust data management practices that support regulatory, operational, and business objectives.

A key responsibility of this role will be driving the data engineering strategy that supports AI Value Realization efforts, including the development of data products and measurement frameworks that quantify the impact of emerging technologies such as ambient listening, clinical documentation tools, and other AI-enabled workflow solutions. Success will be measured through the team's ability to deliver scalable data capabilities that advance research, improve clinical and operational outcomes, and demonstrate measurable value from strategic technology investments.

In addition to leading and developing a high-performing team, the successful candidate will serve as a player-coach, providing hands-on technical leadership and contributing directly to the design, architecture, and delivery of critical data engineering solutions. The role requires a leader who can effectively balance strategic planning, stakeholder engagement, and people management with a willingness to roll up their sleeves and work alongside engineers and architects to solve complex technical challenges, establish best practices, and accelerate delivery of high-priority initiatives.

Responsibilities:

• Strategy Development and Execution:
  Contribute to the development of PennDnA’s data engineering strategy, aligning team objectives with broader organizational goals. Lead and, as needed, assist with multiple complex projects, allocating resources, solving problems, and adjusting plans to achieve desired outcomes.
• Project Leadership:
  Guide planning and execution of data engineering projects, ensuring adherence to timelines, budgets, and quality standards. Coordinate with cross-functional teams. Proactively identify and address risks and issues to mitigate project delays and ensure successful outcomes. Provide hands-on technical expertise where needed to drive to successful outcomes.
• Team Management:
  Manage a team of 6-8 data engineers. Provide direction and feedback. Monitor employee engagement and design interventions, as needed. Contribute to a positive team culture that promotes excellence.
• Stakeholder Management:
  Engage with stakeholders across the organization to understand data requirements and priorities. Communicate with key stakeholders about project scope, team capacity, timelines, and potential risks.
• Collaboration:
  
  Collaborate with PennDnA colleagues and internal clients. Work to understand needs, refine requests, and make recommendations. Negotiate project parameters, when needed.
• Continuous Improvement:
  Look for opportunities to optimize data engineering…