Data Engineer - Enterprise Data Engineering & Analytics

The University of Texas MD Anderson Cancer Center is a leading institution focused on cancer care, research, education, and prevention. They are seeking a Data Engineer to support the design, build, and operationalization of integrated data pipelines and analytics solutions that enable MD Anderson’s digital business initiatives, while contributing to enterprise analytics capabilities.

Responsibilities:

• Participate in end-to-end solution delivery that increases information capabilities and realizes data value across the institution
• Build and test end-to-end data pipelines across ingestion, curation, transformation, modeling, and consumption within the Context Engine framework
• Integrate data governance processes across data provenance, security, data quality, ontology, and metadata management
• Participate in planning, architecture, analysis, design, and build of data pipelines in partnership with IS, Data Offices, and Data Governance teams
• Contribute to existing data pipelines spanning acquisition, integration, and consumption for defined use cases
• Build data curation pipelines including profiling, specification creation, cleansing, transforming, standardizing, mastering, harmonizing, validating, and aggregating data
• Monitor and support data quality across the Context Engine
• Incorporate repeatable solution designs and data models to support reuse and scalability
• Promote effective data management practices and understanding of analytics across the enterprise
• Adhere to IS division standard operating procedures and all MD Anderson policies
• Maintain build standards and governance oversight sign-off aligned with institutional data strategy
• Participate in documentation preparation for enhancements or new technology
• Perform quality control, testing, and peer review of analytics builds
• Support system updates, releases, change control processes, and after-hours support as required
• Train data scientists, analysts, end users, and data consumers on data pipelining and preparation techniques
• Assist in establishing training plans and curricula for Context Engine tools
• Provide institutional, department, and one-on-one training on EDEA deliverables
• Support liaison relationships with customers and OneIS partners to deliver effective technical solutions
• Explore and promote modern tools, techniques, and architectures to automate data preparation and integration tasks
• Improve productivity by reducing manual and error-prone processes
• Model OneIS values through integrity, partnership, quality, and continuous improvement

Requirements:

• Bachelor's Degree
• 2 years Clinical, relevant healthcare information technology, or relevant business experience
• With preferred degree, no experience required
• EPIC - EPIC Certification Must obtain at least one Epic Data Model certification (Clinical, Access, or Revenue) issued by Epic within 180 Days
• Bachelor's in computer science, Master's degree Business Analytics, Computer Science, Information Technology, Data Science, or related
• 3-5 years creating data pipelines in a healthcare research environment
• Experience building and maintaining analytical reports and dashboards
• Problem solving skills and ability to translate business/clinical requirements into reliable data models
• Analytics & reporting- cloud data management solutions like Foundry, Fabric etc
• Data pipeline & ETL development -hands on experience designing, building and maintaining pipelines using python/spark
• Hands-on use of Large Language Models (LLMs) in real-world projects, such as integrating generative AI solutions into applications, workflows, or analytics platforms
• Familiar with prompt engineering, model evaluation, and responsible AI practices
• Experience collaborating with cross-functional teams to deploy and scale LLM-powered features
• EPIC Cogito, Clarity, Caboodle, Clinical Data Model, etc