Data Engineer - Enterprise Data Engineering & Analytics

The Data Engineer role is a key contributor within the Enterprise Data Engineering & Analytics Department, responsible for supporting critical digital initiatives through the design and delivery of scalable data solutions. The Data Engineer develops and maintains end-to-end data pipelines within the Context Engine, working collaboratively with cross-functional teams to ensure high-quality data integration and analytics delivery. The Data Engineer plays an essential role in accelerating access to trusted data and enabling actionable insights that support institutional priorities.
UT MD Anderson is a leading institution focused on cancer care, research, education, and prevention. The Data Engineer at UT MD Anderson contributes to advancing innovative data strategies that support clinical, operational, and research excellence, ensuring data is transformed into meaningful, secure, and reliable assets across the organization.
The ideal candidate will have a strong foundation in data engineering principles with a preferred Master’s degree in Business Analytics, Computer Science, Information Technology, Data Science, or a related field. Experience building end-to-end data pipelines and familiarity with tools such as Power BI, Microsoft Fabric, and Palantir Foundry are highly desirable. Candidates with healthcare IT or clinical data experience and certifications such as Caboodle will stand out.
Minimum $106,500 – Midpoint $133,000 – Maximum $159,500
Work Location: Remote 100% within Texas

Why Us?
Working as a Data Engineer at UT MD Anderson provides the opportunity to directly impact innovative healthcare data initiatives that improve patient outcomes and accelerate research. This role offers strong collaboration with senior technical teams, continuous learning, and exposure to modern data platforms while supporting a mission-driven organization that values excellence and sustainable work-life balance.
• Employer-paid medical coverage starting day one for employees working 30+ hours/week, plus optional group dental, vision, life, AD&D, and disability insurance.
• Accruals for PTO and Extended Illness Bank, plus paid holidays, wellness, childcare, and other leave options.
• Tuition Assistance Program after six months of service and access to extensive wellness, fitness, and employee resource groups.
• Defined-benefit pension through the Teachers Retirement System, voluntary retirement plans, and employer-paid life and reduced salary protection programs.
Responsibilities
Data Engineering – End-to-End Solution Delivery:
• Participate in end-to-end solution delivery to enhance information capabilities and data value across the institution
• Support data ingestion, ingress, egress, curation, transformation, modeling, and visualization within the Context Engine framework
• Integrate data governance processes ensuring tracking of provenance, data security, quality, and ontology
• Contribute to planning, architecture, analysis, design, and build of data pipelines in collaboration with IS, Data Offices, and governance teams
• Support pipelines across all stages from data acquisition to consumption for use-case-driven analytics
• Develop repeatable solution designs and data models supporting scalable analytics deliverables
• Build data curation pipelines including profiling, cleansing, transforming, standardizing, harmonizing, validating, and aggregating data
• Monitor and maintain data quality across enterprise systems
• Incorporate metadata management and governance standards into all data processes
• Promote modern tools, automation, and architectures to reduce manual and error-prone data integration tasks
Standards, Testing & System Maintenance:
• Adhere to IS division procedures and institutional data strategy standards including governance oversight
• Support documentation for enhancements and new technologies
• Follow change control processes and participate in change control audits
• Perform quality control, testing, and peer reviews of data solutions
• Assist in managing analytics system updates and new releases
• Ensure compliance with regulatory requirements, quality standards, and best practices
• Collaborate with internal and external stakeholders for solution validation
• Participate in after-hours support and downtime procedures
Education & Training:
• Train data scientists, analysts, and end users on data pipeline utilization and preparation techniques
• Assist in developing training plans for Context Engine tools in partnership with training teams
• Deliver institutional, departmental, and individual training on data engineering deliverables
• Support the development of training curricula with system experts
Collaboration & Customer Support:
• Build strong partnerships with Enterprise Development & Integration and Data Science teams
• Support liaison relationships across departments and IS teams
• Deliver effective technical solutions aligned with customer needs
• Provide responsive support and maintain high standards of customer service
OneIS Commitment:
• Promote integrity, trust, and respect in all interactions
• Strengthen collaborative relationships with stakeholders and team members
• Deliver innovative, high-quality, and sustainable IT solutions
• Demonstrate commitment to continuous improvement and operational excellence