Enterprise Data Architect

Emory Healthcare•Tucker, GA

5h•$139,027 - $172,823•Remote

About The Position

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, and executive stakeholders to develop a modern, scalable, and trusted data ecosystem. 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.

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.
Enterprise Architecture Experience: architecture leadership on at least one modern cloud data platform, preferably Microsoft Fabric or Databricks, with responsibility for defining standards, reference architectures, and platform strategy.
Data Modeling Expertise: Strong experience in enterprise data modeling, including dimensional modeling, conceptual and logical data models, canonical data models, and designing reusable data structures for analytics and reporting.
Modern Data Platform Design: experience designing and implementing data lakes, lakehouses, data warehouses, semantic layers (semantic models and Ontologies), and enterprise data products.
Data Engineering Background: 10+ years of experience in data engineering, data architecture etc. -- not being primarily focused on coding pipelines, workload migrations, report development, or day-to-day ETL implementation.
Strong communication, leadership, facilitation, and stakeholder management skills.
Ability to translate complex business requirements into enterprise architecture solutions.

Nice To Haves

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.
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.

Responsibilities

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.
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.
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.
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.
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.
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.
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.