Principal Data Architect, Research

This is a fully remote position, open to applicants in Colorado, +6 more states.

📋 Description

• Provide leadership at the principal level for data architecture within the research domain.

• Design and oversee scalable, secure, and research-ready data structures for analytics, artificial intelligence, and compliance with regulatory data use.

• Create the enterprise research data platform, incorporating lakehouse architecture.

• Establish architecture standards for data ingestion, transformation, curation, and publication.

• Convert Epic Clarity and Caboodle source structures into architectural patterns ready for research and reusable platform components.

• Develop a cloud-native architecture that supports security, resilience, performance, and regulatory requirements.

• Align platform roadmaps and design decisions with the enterprise data strategy, research priorities, and stakeholder needs.

• Create dimensional and normalized data models for research reporting, analysis, and reuse.

• Transform clinical, operational, device, and waveform data into structured datasets and semantic layers.

• Define canonical data models and shared business definitions.

• Implement interoperability standards, including OMOP.

• Maintain model integrity across curated datasets, marts, and downstream analytical assets.

• Establish governance frameworks that encompass lineage, stewardship, access controls, retention, and accountability.

• Ensure adherence to HIPAA, institutional review board requirements, and organizational policies.

• Define and implement de-identification, privacy, and security strategies for protected health information and sensitive research data.

• Lead data quality processes, validation controls, and stewardship practices.

• Collaborate with compliance, legal, privacy, and research administration stakeholders.

• Design data pipelines and platform services suitable for machine learning, generative AI, and advanced analytics.

• Structure and optimize datasets for model training, validation, inference, and controlled experimentation.

• Facilitate scalable analytical environments for repeatable research workflows and exploratory analysis.

• Assist research teams in assessing analytical use cases and determining data design approaches.

• Establish architecture standards for responsible AI, reproducibility, transparency, and model governance.

• Mentor engineers, architects, and analysts.

• Influence enterprise data standards and architectural direction through design reviews and standards development.

• Collaborate with information technology, clinical, operational, and research stakeholders.

• Review technical designs and recommend architecture patterns that are scalable, maintainable, and compliant.

• Advocate for modern data architecture methodologies, tools, and delivery practices across the enterprise research ecosystem.

• Report to the Manager of Data Analytics (Research).

⛳️ Requirements

• Bachelor's degree in a related field.

• Over 5 years of relevant job experience.

• Candidates must reside in Georgia, Texas, Tennessee, North Carolina, Florida, South Carolina, Michigan, or Colorado.

• Familiarity with research data platform architecture, including lakehouse architecture.

• Experience with Epic Clarity and Caboodle.

• Proven experience in developing dimensional and normalized data models.

• Knowledge of clinical, operational, device, and waveform data.

• Understanding of interoperability standards, including OMOP.

• Experience in data governance, lineage, stewardship, access controls, retention, and accountability.

• Knowledge of HIPAA and institutional review board requirements.

• Experience with de-identification, privacy, and security strategies for protected health information.

• Proven track record in directing data quality processes and validation controls.

• Experience in designing data pipelines and platform services for machine learning, generative AI, and advanced analytics.

• Experience in structuring datasets for model training, validation, inference, and controlled experimentation.

• Knowledge of responsible AI, reproducibility, transparency, and model governance.

• Ability to mentor engineers, architects, and analysts.

• Capability to conduct design reviews and develop standards.

• Ability to collaborate with information technology, clinical, operational, and research stakeholders.

🏝️ Benefits

• Medical, dental, vision, and prescription drug coverage.

• Retirement savings plans with employer contributions.

• Life insurance.

• Disability coverage.

• Paid time off.

• Holidays.

• Family leave benefits.

• Tuition reimbursement.

• Professional development programs.

• Opportunities for advancement.

• Employee Assistance Program (EAP).

• Wellness initiatives.

• Discounts on services.