[Hiring] Enterprise Data Architect @Emory Healthcare

Role Description

The Enterprise Data Architect serves as the strategic and technical leader for enterprise data architecture, cloud data platforms, semantic modeling, ontologies, governance, and AI-ready data ecosystems across the organization. You will be responsible for defining, governing, and advancing the enterprise data architecture strategy to support analytics, business intelligence, artificial intelligence (AI), operational reporting, research, and digital transformation initiatives across the organization. This role serves as the senior technical authority for enterprise data architecture, cloud data platforms, data modeling, integration architecture, metadata management, and master data management.

The Enterprise Data Architect partners closely with:

• Business leaders
• Data engineers
• Analytics teams
• Data scientists
• Application teams
• Governance committees
• Executive stakeholders

The role ensures enterprise data assets are secure, governed, discoverable, interoperable, and optimized for self-service analytics, AI, and operational decision-making. The architect leads the design and implementation of cloud-based data platforms and modern data architectures leveraging technologies such as Microsoft Fabric, Databricks, data lakes, lakehouses, and enterprise semantic layers.

Responsibilities

• Enterprise Data Architecture:
  • Define and maintain the enterprise data architecture roadmap aligned with organizational goals and technology strategy.
  • Develop and maintain conceptual, logical, and physical enterprise data models.
  • Establish enterprise data standards, principles, patterns, and reference architectures.
  • Design future-state architectures that support business growth, scalability, interoperability, regulatory compliance, and innovation.
  • Assess current-state data architecture and identify opportunities for modernization and optimization.
  • Lead architecture reviews and provide guidance for enterprise initiatives involving data, analytics, integration, and AI.
• Cloud Data Platform Architecture:
  • Design and govern modern cloud data platforms utilizing technologies such as Microsoft Fabric, Databricks, and other enterprise analytics platforms.
  • Define architecture patterns for data lakes, lakehouses, data warehouses, semantic models, and data products.
  • Establish architectural standards for real-time, batch, streaming, and event-driven data processing.
  • Guide platform adoption and ensure alignment with enterprise architecture standards.
  • Evaluate emerging technologies and determine their applicability to business and technical needs.
• Data Modeling and Information Architecture:
  • Lead enterprise data modeling efforts across operational, analytical, and AI use cases.
  • Define standards for dimensional, relational, and canonical data models.
  • Ensure consistency and alignment between business processes and enterprise information models.
  • Establish enterprise data domains and business subject area models.
  • Support data product development through reusable and standardized data structures.
• Semantic Layer and Ontology Architecture:
  • Design, develop, and govern enterprise shared semantic layer assets that provide consistent business definitions and metrics across the organization.
  • Lead development of certified semantic models, enterprise metrics layers, business glossaries, taxonomies, ontologies, and knowledge models.
  • Create reusable semantic assets that support Power BI, Microsoft Fabric, AI applications, enterprise reporting, self-service analytics, and agent-based solutions.
  • Establish governance processes for semantic model certification, lifecycle management, and adoption.
  • Ensure semantic assets are discoverable, trusted, reusable, and aligned with data governance standards.
  • Define enterprise ontology frameworks that support AI, knowledge retrieval, intelligent search, and organizational knowledge management.
  • Promote a shared semantic understanding across business units to improve consistency in analytics and decision-making.
• Data Governance and Information Management:
  • Partner with data governance teams to establish and enforce enterprise data standards.
  • Support enterprise data stewardship, data quality, metadata management, and information lifecycle management initiatives.
  • Develop and maintain standards for data lineage, business metadata, technical metadata, and data cataloging.
  • Ensure compliance with privacy, security, and regulatory requirements.
  • Promote best practices for enterprise information management and data governance.
• Master Data Management:
  • Lead architecture and governance efforts related to master data and reference data management.
  • Define enterprise master data strategies and integration approaches.
  • Support maintenance of enterprise hierarchies, reference data structures, and canonical business entities.
  • Collaborate with governance teams to improve data consistency across systems.
• Analytics, AI, and Data Products:
  • Design architectures that enable advanced analytics, machine learning, generative AI, and intelligent automation solutions.
  • Support development of AI-ready data platforms, retrieval architectures, semantic indexes, and knowledge repositories.
  • Establish standards for reusable enterprise data products and analytics assets.

Qualifications

• Experience with modern cloud data platforms such as Microsoft Fabric, Databricks, or Snowflake.
• Strong expertise in enterprise data modeling, dimensional modeling, data lakehouse architectures, and data integration.
• Experience building and governing certified semantic models, enterprise semantic layers, and shared analytics assets.
• Experience developing business ontologies, taxonomies, knowledge models, and business glossary frameworks.
• Knowledge of metadata management, data lineage, data catalogs, and data governance frameworks.
• Knowledge of ETL, ELT, API integration, real-time streaming, and event-driven architectures.
• Understanding of AI, machine learning, generative AI, and knowledge retrieval architectures.
• Strong communication, leadership, facilitation, and stakeholder management skills.
• Ability to translate complex business requirements into enterprise architecture solutions.
• Healthcare industry experience including clinical, financial, operational, research, population health, or healthcare analytics domains and exposure to EHR systems such as EPIC.
• Knowledge of healthcare interoperability standards such as HL7, FHIR, and industry data models.

Requirements

• Minimum Education: Bachelor's degree in computer science, information management, or related field.
• Minimum Experience: 10 years of experience with data warehousing or similar IT application. Prior experience in IT architecture designing and implementing roles in large-scale, distributed, and complex IT environments.

Benefits

• Comprehensive health benefits that start day 1.
• Student Loan Repayment Assistance & Reimbursement Programs.
• Family-focused benefits.
• Wellness incentives.
• Ongoing mentorship, development, leadership programs...and more!

Company Description

Emory is an equal opportunity employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by state or federal law.

Emory Healthcare is committed to providing reasonable accommodations to qualified individuals with disabilities upon request. Please contact Emory Healthcare’s Human Resources at [email protected]. Please note that one week's advance notice is preferred.