Senior Data Engineer - Healthcare AI

Within the Data Impact & Governance Department, the Senior Data Engineer for Healthcare Artificial Intelligence role supports the design and delivery of data infrastructure that powers advanced artificial intelligence and machine learning solutions in healthcare. This is more than engineering-it's an opportunity to shape the future of cancer care through responsible AI innovation.

Salary Range is Min-$123,000, Mid-$154,000, Max-$185,000, 100% remote within Texas.

The Data Impact & Governance Department focuses on building scalable, secure, and trusted data platforms that enable innovation while maintaining compliance and governance standards. The Senior Data Engineer for Healthcare Artificial Intelligence will architect and optimize critical data systems, the Senior Data Engineer for Healthcare Artificial Intelligence will enable responsible AI adoption, and the Senior Data Engineer for Healthcare Artificial Intelligence will help advance data-driven improvements in cancer care across UT MD Anderson.

The ideal candidate brings advanced technical expertise in data engineering, healthcare data management, and AI/ML enablement. Preferred qualifications include experience with Python, SQL, Spark, Azure services, Infrastructure-as-Code technologies, CI/CD workflows, healthcare data standards such as HL7, FHIR, and DICOM, HIPAA/HITRUST compliance practices, feature and vector store management, leadership experience, and the ability to communicate effectively with both technical and non-technical stakeholders.
Why Us?
At UT MD Anderson, this role provides the opportunity to build the data foundations that support transformative artificial intelligence and machine learning initiatives in healthcare. The position contributes directly to improving patient outcomes through responsible innovation while offering meaningful professional growth, collaboration with multidisciplinary experts, and resources that support long-term career development and 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
Build and Scale AI/ML Data Pipelines
Design, implement, and maintain batch and streaming pipelines for machine learning training, deployment, inference, and monitoring
Utilize Azure, Dataiku, and open-source tools to deliver scalable and reliable AI/ML data solutions
Data, Feature, and Vector Store Engineering Deploy and manage raw data stores for production AI/ML workloads
Develop and maintain feature stores to support machine learning lifecycle requirements
Manage vector stores to provide fast and reliable access for production AI systems

Automate Infrastructure and Ensure Data Trust
Automate deployments using Infrastructure-as-Code methodologies
Implement CI/CD workflows to improve operational efficiency and deployment reliability
Establish data validation processes to improve accuracy and consistency
Implement lineage, anomaly detection, and drift monitoring capabilities for compliant and trusted data operations

Security, Compliance, and Operations
Enforce encryption controls across data platforms and pipelines
Implement role-based access controls, tokenization, and audit logging
Support HIPAA and HITRUST compliance requirements while enabling scalable AI operations
Manage monitoring, alerting, incident response, and continuous improvement activities
Own data pipelines and infrastructure throughout the operational lifecycle

Collaboration and Leadership
Partner with data engineers, machine learning engineers, data scientists, clinicians, and technology stakeholders to deliver scalable AI solutions
Mentor team members and promote data engineering best practices across the organization
Perform additional duties as assigned in support of departmental objectives

Required Education: Bachelor's degree.

Preferred Education: Master's Level Degree

Preferred Certification: Must obtain at least one Epic Data Model certification (Clinical, Access, or Revenue) issued by Epic within 180 days of date of entry into job.

Preferred Certification: Any of the following:

Azure Data Engineer Associate (DP-203),
EPIC Cogito Certification,
HIPAA Privacy & Security Certification,
HL7/FHIR Certification.

Required Experience: Five years of relevant information technology experience. May substitute required education with years of related experience on a one-to-one basis. With preferred degree, three years of experience required.

Preferred Experience: Healthcare experience in AI/ML space is a must, two years of industry experience in a Senior Data Scientist role, knowledge of data privacy, security, and HIPAA compliance in healthcare.

The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.

This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.

It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law.