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# Data Architect

- **Company:** Tempus Inc
- **Location:** Chicago, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $140,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Cerner, Third Normal Form, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, BigQuery, Health Informatics, Clinical Data Repository, Cloud Engineering, Data Architecture, Data Dictionary, Information Engineering, Data Infrastructure, Data Integrity, Data Profiling, Data Security, Data Structures, Data Systems, Data Vault Modeling, Relational Databases, Software Debugging, Dicom, Entity Relationship Models, Graph Database, Interoperability, Python (Programming Language), NoSQL, Software Tools, Lucidchart, Standard Sql, Unstructured Data, Openapi, Pinecone, Sql Optimization, Fast Healthcare Interoperability Resources, Retrieval-Augmented Generation, System Availability, Agentic-AI, Microsoft Fabric, Pgvector, Information Technology, Star Schema, Health Level Seven International, Weaviate, Static Data
- **Published:** October 16, 2026
- **Apply:** https://startup.jobs/data-architect-next-platform-tempus-ai-inc-8818963

## About the Role

* Education: Bachelor's Degree in Computer Science, Health Informatics, or a related field.
 * Professional Experience: 7+ years in data architecture or enterprise modeling, with significant experience in the healthcare or life sciences domain.
 * Modeling Mastery: Expert-level knowledge of 3NF, Dimensional (Star Schema), and Data Vault 2.0 modeling techniques.
 * Advanced SQL & Python: Exceptional SQL skills for complex analytical environments and proficiency in Python for data profiling and debugging.
 * Technology Strategy: Ability to articulate the trade-offs between RDBMS, MPP, and NoSQL technologies, and experience implementing Master Data Management (MDM) solutions.
 * Interoperability Standards: Deep familiarity with HL7, FHIR, and Epic/Cerner data structures.
 * Tools: Proficiency with modeling tools such as Erwin, Vertabelo, or Lucidchart.

Bonus Points

 * AI Data Engineering: Experience with Vector databases (e.g., Pinecone, Weaviate, or pgvector) or Graph databases to support RAG and agentic memory.
 * Cloud Architecture: Hands-on experience with GCP (BigQuery, Vertex AI) or AWS healthcare-native services.
 * Specialized Clinical Data: Direct experience working with EHR, OMOP, DICOM, genomic data models, or longitudinal patient records.

## Description

We are seeking a Data Architect to design the structural backbone of a high-scale, multi-modal healthcare data ecosystem. You will be responsible for architecting data environments that serve massive networks of hospitals, ensuring that diverse data types-ranging from structured EHR records to unstructured genomic and imaging data-are seamlessly accessible to autonomous AI agents.

Our goal is to move beyond static data warehousing to create a dynamic, "agent-ready" data fabric that supports real-time clinical evaluation at an enterprise scale.

What You'll Do

 * Multi-Modal Architecture: Lead the design and management of an enterprise data model that integrates complex domains including clinical EHR records, high-throughput genomics (NGS), and cardiovascular imaging (Echo, Cath, ECG).
 * Scale for Hospital Networks: Architect data solutions designed to scale across federated networks of hospitals, ensuring multi-tenancy, high availability, and performance across hybrid cloud environments.
 * Enable Agentic Workflows: Design data access patterns and metadata layers specifically optimized for AI agents, allowing them to autonomously discover, query, and reason over structured and unstructured datasets.
 * Schema & API Ownership: Author and maintain entity-relationship diagrams (ERDs), data dictionaries, and API specifications across multiple technologies (Relational, NoSQL, Vector Databases).
 * Data Quality & Traceability: Implement automated solutions to monitor data quality and lineage with strict traceability back to source systems, ensuring "ground truth" for agentic evaluations.
 * Technical Advocacy: Educate engineering and clinical teams on data modeling standards, governance, and best practices for maintaining data integrity in a HIPAA-regulated environment.