Data Engineer - Enterprise Data Engineering & Analytics

UT MD Anderson Cancer Center•Houston, TX

3d•Onsite

About The Position

The Data Engineer role is a pivotal position within the Enterprise Data Engineering & Analytics Department, supporting the design, build, and operationalization of integrated data pipelines and analytics solutions that enable MD Anderson’s digital business initiatives. The Data Engineer works across the Context Engine framework to deliver end-to-end data engineering solutions while partnering closely with Enterprise Data Engineering & Analytics teams and other institutional stakeholders. The Data Engineer contributes to the mission of MD Anderson Cancer Center, a leading institution focused on cancer care, research, education, and prevention. In this role, the Data Engineer helps advance enterprise analytics capabilities by ensuring secure, governed, and reusable data assets that accelerate insights and improve time-to-solution across MD Anderson.

Requirements

Bachelor's Degree
2 years Clinical, relevant healthcare information technology, or relevant business experience.
Must obtain at least one Epic Data Model certification (Clinical, Access, or Revenue) issued by Epic. within 180 Days
Must pass pre-employment skills test as required and administered by Human Resources.

Nice To Haves

Advanced education in analytics or computer science
Hands-on experience building data pipelines in healthcare or research environments
At least 2 years of Cloud Data Management Framework (Foundry/Fabric) experience
Hands-on use of Large Language Models (LLMs) in real-world projects
Python or Spark development
Analytics delivery
Epic certification
Ability to collaborate across technical and clinical teams
Bachelor's in computer science
Master's degree Business Analytics, Computer Science, Information Technology, Data Science, or related.
With preferred degree, no experience required.
May substitute required education with years of related experience on a one to one basis.
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.
Candidates should be familiar with prompt engineering, model evaluation, and responsible AI practices.
Experience collaborating with cross-functional teams to deploy and scale LLM-powered features is highly desirable.
Preferred certifications: EPIC Cogito, Clarity, Caboodle, Clinical Data Model, etc.
Prefer a candidate in Houston Texas or surrounding area.

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

Benefits

Employer-paid medical coverage starting day one for employees working 30+ hours/week
Optional group dental, vision, life, AD&D, and disability insurance.
Accruals for PTO and Extended Illness Bank
Paid holidays
Wellness
Childcare
Other leave options.
Tuition Assistance Program after six months of service
Access to extensive wellness, fitness, and employee resource groups.
Defined-benefit pension through the Teachers Retirement System
Voluntary retirement plans
Employer-paid life and reduced salary protection programs.