Lead-Data Engineer

The Lead Data Engineer is responsible for designing, building, and supporting enterprise data solutions that enable clinical research, operational excellence, analytics, and data-driven decision-making. This role translates complex business and research requirements into scalable, secure, and maintainable data architectures, integration solutions, and analytics platforms using established development standards, tools, and best practices. In addition to technical leadership, the Lead Data Engineer is expected to drive strategic data initiatives, proactively identify opportunities for process improvement, automation, AI adoption, and data modernization, and provide expert guidance on emerging technologies that support clinical research and institutional objectives.

The Lead Data Engineer serves as a technical lead for complex projects, providing end-to-end ownership from requirements gathering and solution design through implementation, documentation, deployment, and operational support. The role requires strong prioritization, stakeholder engagement, communication, and project management skills to successfully manage multiple initiatives and ensure timely delivery of high-quality solutions. This position is responsible for ensuring solutions meet functional, technical, governance, security, and compliance requirements while adhering to St.

Jude development standards and architectural principles. The Lead Data Engineer also monitors and optimizes databases, data warehouses, cloud platforms, ETL pipelines, and scheduling processes to ensure system reliability, performance, scalability, and data quality.

The successful candidate will demonstrate leadership through proactive communication, meaningful participation in cross-functional discussions, high-quality technical documentation, mentorship of team members, and a commitment to continuous improvement. Experience with AI-enabled technologies, advanced analytics, and data engineering solutions supporting clinical research, healthcare, or regulated environments is highly desirable.

• Participate in the development of enterprise data and analytics strategies that support clinical research, operational excellence, AI-enabled innovation, and organizational objectives.
• Provide expert leadership and guidance on industry best practices related to data engineering, data architecture, data integration, data governance, cloud platforms, analytics, and reporting.
• Lead the design, implementation, and optimization of scalable data models, data warehouses, and data integration solutions that enable efficient access, analysis, visualization, and sharing of data.
• Serve as a technical lead for data modernization and digital transformation initiatives, including the Clinical Research Workbench and broader data modernization efforts, by providing architectural guidance, driving adoption of modern data and AI technologies, identifying opportunities for process innovation, and ensuring alignment with institutional research, analytics, governance, and operational objectives.
• Translate complex business, research, and operational requirements into secure, scalable, and maintainable technical solutions using established development standards, tools, and processes.
• Drive end-to-end ownership of data engineering initiatives, including requirements gathering, architecture, development, testing, implementation, documentation, deployment, operational support, and continuous improvement.
• Implement and monitor data governance practices to ensure compliance with institutional standards, regulatory requirements, security policies, and data quality expectations.
• Collaborate with researchers, business stakeholders, domain experts, and IT teams to develop secure and effective data sharing, analytics, and reporting solutions.
• Evaluate operational processes, business needs, and research requirements to identify opportunities for data modernization, automation, process improvement, and platform optimization.
• Research, evaluate, and recommend emerging technologies, advanced analytics capabilities, AI/ML solutions, and data engineering tools that deliver measurable value to the institution.
• Identify opportunities to leverage Artificial Intelligence, Generative AI, advanced analytics, and automation technologies to improve clinical research operations, data management, and decision‑making processes.
• Investigate, troubleshoot, and resolve complex data integration, performance, quality, and architecture…