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. Job Responsibilities: 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 issues across on-premises and cloud-based environments. Monitor and optimize databases, data warehouses, ETL/ELT pipelines, data ingestion frameworks, and cloud platforms to ensure reliability, scalability, performance, security, and operational excellence. Lead complex technical projects and analytics initiatives from concept to deployment, including dashboard development, advanced data visualization, automated reporting, coding, testing, debugging, documentation, and support. Proactively manage project timelines, dependencies, risks, issues, and stakeholder communications while balancing multiple priorities and competing demands. Provide clear, concise, and leadership-ready status updates, escalating risks early and recommending practical solutions to maintain project success. Develop and maintain high-quality technical documentation, solution designs, architecture diagrams, operational procedures, support documentation, and knowledge-transfer materials. Mentor, coach, and develop junior team members by promoting technical excellence, knowledge sharing, best practices, and professional growth. Minimum Education and/or Training: Bachelor's degree in computer science, data science, information science, business, or related field required. Master's degree preferred. Minimum Experience: Minimum requirement: 5+ progressive years of IT experience including strong database design, data architecture and data transform processes. Experience in data architecture and design. Some experience with data governance including implementation of a data governance framework. Experience with leading industrial tools for data lake, big data, or ETL. Experience with enterprise reporting tools for development of reports as applicable. Experience creating dashboards using data visualization tools (e.g. PBI, Tableau, Qlik, and data warehousing applications) and source control systems (e.g., Epic, Workday) as applicable. Some experience and knowledge in Master Data Management and Data Dictionary Standards. Some experience with regulatory requirements and standards as applicable. Experience providing technical guidance and mentorship within Data Engineering. Proven performance in earlier role/comparable role. Other Desirable Qualifications: Experience evaluating or working with Epic is highly desirable, especially where that experience includes translating clinical, administrative, or operational workflow pain points into product requirements, business cases, or adoption plans preferred. Working experience with Common data models like I2B2/OMOP/PCORnet is preferred Working knowledge of commercially available AI and GenAI solutions, including healthcare products and enterprise copilots or assistants preferred. Experience with other enterprise platforms such as Microsoft, ServiceNow, Workday, or comparable healthcare and business technology products preferred. Licensure, Registration and/or Certification Required by Law: None Licensure, Registration and/or Certification Required by SJCRH Only: None Compensation In recognition of certain U.S. state and municipal pay transparency laws, St. Jude is including a reasonable estimate of the compensation range for this role. This is an estimate offered in good faith and a specific salary offer takes into account factors that are considered in making compensation decisions including but not limited to skill sets, experience and training, licensure and certifications, and other business and organizational need