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Senior Data Engineer - Healthcare AI
In the Data Impact & Governance Department, you’ll architect and build the data infrastructure that powers cutting-edge AI and machine learning solutions for healthcare. This is more than engineering—it’s an opportunity to shape the future of cancer care through responsible AI innovation.
What’s in it for you?
- Paid Medical Benefits: MD Anderson covers 100% of medical benefits for employees, plus dental and vision options.
- Generous Paid Time Off (PTO): Vacation, sick leave, and holidays to help you recharge.
- Retirement Plans: Secure your future with robust retirement programs and employer contributions.
- Professional Growth: Access to advanced training, leadership development, and opportunities to work on transformative AI projects.
- Mission-Driven Work: Your expertise will enable AI-driven insights that improve patient outcomes and operational efficiency.
The ideal candidate for the Senior Data Engineer – Healthcare AI position is a highly skilled data engineering professional with deep expertise in building scalable, secure, and high-performance data pipelines for AI/ML applications. They possess a strong understanding of healthcare data standards and compliance requirements, combined with advanced technical proficiency in cloud platforms, orchestration tools, and feature/vector store management. This individual thrives in collaborative environments, demonstrates leadership in mentoring others, and is passionate about enabling responsible AI innovation in healthcare.
Key Attributes of the Ideal Candidate:
- Technical Mastery: Expert in Python, SQL, Spark, and modern data engineering frameworks; proficient in Azure services, IaC tools (Terraform, Bicep), and CI/CD workflows.
- AI/ML Data Expertise: Experienced in designing and managing feature and vector stores, batch and streaming pipelines, and high-throughput data architectures for AI/ML systems.
- Healthcare Data Knowledge: Familiar with HL7, FHIR, DICOM standards and skilled in handling EHR, imaging, and clinical datasets with de-identification and compliance.
- Security & Compliance Focus: Strong understanding of HIPAA/HITRUST requirements and ability to implement encryption, RBAC, and audit logging.
- Leadership & Collaboration: Capable of mentoring team members, driving best practices, and partnering with clinicians, data scientists, and IT teams to deliver impactful solutions.
- Problem-Solving & Innovation: Adept at troubleshooting complex data challenges, optimizing performance, and exploring emerging technologies for scalable AI operations.
- Communication Skills: Able to clearly document processes and present technical concepts to both technical and non-technical audiences.
Key Responsibilities
Build and Scale AI/ML Data Pipelines
- Design, implement, and maintain batch and streaming pipelines for ML training, deployment, inference, and monitoring using Azure, Dataiku, and open-source tools.
Data, Feature, and Vector Store Engineering
- Deploy and manage raw data, feature, and vector stores to enable fast, reliable access for production AI/ML systems.
Automate Infrastructure and Deployments
- Use Infrastructure-as-Code (IaC) and CI/CD workflows to automate deployments, improving reliability and efficiency.
Ensure Data Quality and Trust
- Implement validation, lineage, anomaly detection, and drift monitoring to deliver accurate, compliant data.
Security and Compliance by Design
- Enforce encryption, RBAC, tokenization, and audit logging to ensure HIPAA/HITRUST compliance while enabling scalable AI operations.
Collaborate and Lead
- Partner with data engineers, ML engineers, data scientists, and clinical stakeholders to deliver scalable AI solutions.
- Mentor team members and drive best practices in data engineering.
Own and Operate
- Manage pipelines and infrastructure end-to-end, including monitoring, alerting, incident management, and continuous improvement.
Other Duties
- Perform additional tasks as assigned to support departmental goals.
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.
