Principal Data Architect, Research

GMH

. Provide principal-level data architecture leadership for the research domain .

Posted 9/29/2026full-timeRemote • United StatesLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in data architecture leadership, focusing on scalable and secure data structures for analytics and compliance. Proficient in developing data models, governance frameworks, and cloud-native architectures to support research and advanced analytics.

Highest-signal resume keywords

Data Architecture LeadershipLakehouse ArchitectureEpic Clarity and CaboodleData Governance and StewardshipMachine Learning and Advanced Analytics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingData IngestionData TransformationData CurationData PublicationInteroperability StandardsDe-identification StrategiesData Quality ProcessesData Pipeline DesignCanonical Data Models

Soft Skills

MentoringCollaborationInfluencingDesign ReviewStakeholder Engagement

Tools & Technologies

Cloud-Native ArchitectureResearch Data PlatformAnalytics ToolsGenerative AIMachine Learning Frameworks

Industry Keywords

HIPAA ComplianceInstitutional Review BoardResearch DataOperational DataClinical Data

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Provide principal-level data architecture leadership for the research domain
Design and govern scalable, secure, and research-ready data structures for analytics, artificial intelligence, and regulatory-compliant data use
Design the enterprise research data platform, including lakehouse architecture
Define architecture standards for data ingestion, transformation, curation, and publication
Translate Epic Clarity and Caboodle source structures into research-ready architectural patterns and reusable platform components
Establish cloud-native architecture supporting security, resilience, performance, and regulatory requirements
Align platform roadmaps and design decisions with enterprise data strategy, research priorities, and stakeholder requirements
Develop dimensional and normalized data models for research reporting, analysis, and reuse
Translate clinical, operational, device, and waveform data into structured datasets and semantic layers
Define canonical data models and shared business definitions
Apply interoperability standards, including OMOP
Maintain model integrity across curated datasets, marts, and downstream analytical assets
Establish governance frameworks covering lineage, stewardship, access controls, retention, and accountability
Ensure compliance with HIPAA, institutional review board requirements, and organizational policies
Define and apply de-identification, privacy, and security strategies for protected health information and sensitive research data
Direct data quality processes, validation controls, and stewardship practices
Partner with compliance, legal, privacy, and research administration stakeholders
Design data pipelines and platform services for machine learning, generative AI, and advanced analytics
Structure and optimize datasets for model training, validation, inference, and controlled experimentation
Enable scalable analytical environments for repeatable research workflows and exploratory analysis
Support research teams in evaluating analytical use cases and selecting data design approaches
Define 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 scalable, maintainable, and compliant architecture patterns
Promote modern data architecture methods, tools, and delivery practices across the enterprise research ecosystem
Report to the Manager, Data Analytics (Research)

Requirements

What you’ll need

Bachelors degree in related field
5+ years of job related experience
Candidates must reside in Georgia, Texas, Tennessee, North Carolina, Florida, South Carolina, Michigan, or Colorado
Knowledge of research data platform architecture, including lakehouse architecture
Experience with Epic Clarity and Caboodle
Experience developing dimensional and normalized data models
Knowledge of clinical, operational, device, and waveform data
Knowledge of interoperability standards, including OMOP
Experience with 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
Experience directing data quality processes and validation controls
Experience designing data pipelines and platform services for machine learning, generative AI, and advanced analytics
Experience structuring datasets for model training, validation, inference, and controlled experimentation
Experience with responsible AI, reproducibility, transparency, and model governance
Ability to mentor engineers, architects, and analysts
Ability to conduct design reviews and develop standards
Ability to collaborate with information technology, clinical, operational, and research stakeholders

Benefits

Comp & perks

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