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Stardog Admin

MagicforceChicago, IL🇺🇸United StatesPosted 15 Sept 2026

Why This Role Stands Out

This hybrid role offers significant growth potential as you will be instrumental in advancing a cutting-edge knowledge graph platform for critical healthcare initiatives. You'll thrive here if you possess strong Stardog administration and SPARQL expertise, and enjoy collaborating with diverse teams to drive impactful data solutions. Apply today to shape the future of healthcare analytics!

Quick Overview

Seniority
Mid Senior
Work mode
Hybrid
Location
Chicago, IL, United States
Posted
Yesterday
SQLSQL ServerAzureDatabricksPostgreSQL

Job Description

Mandatory Skills:

Stardog, Knowledge Graph, Stardog Administration, SPARQL , RDF, OWL, RDFS, SHACL

Desired Skills:

Databricks, Postgress, Azure, AKS, Admin, Recovery

Job Description:

We are seeking a Senior Stardog Database Administrator (DBA) / Knowledge Graph Engineer with 3+ years of experience administering Stardog or comparable graph database platforms to support enterprise healthcare data and analytics initiatives.

This role is responsible for managing, optimizing, and advancing the organization's knowledge graph platform to enable healthcare analytics, interoperability, data governance, semantic integration, and AI-driven solutions. The successful candidate will work closely with data engineers, architects, clinical informatics teams, analysts, product owners, and software engineers to support operational, financial, quality, and clinical healthcare use cases.

Experience with Microsoft SQL Server, PostgreSQL, Databricks, or modern cloud-based analytics platforms is highly preferred.

Key Responsibilities

Knowledge Graph & Graph Database Administration

· Install, configure, monitor, upgrade, and maintain Stardog environments across development, test, and production environments.

· Ensure platform availability, scalability, security, reliability, and performance.

· Administer enterprise knowledge graph solutions supporting healthcare analytics, interoperability, benchmarking, quality reporting, and AI initiatives.

· Optimize SPARQL queries, semantic reasoning performance, indexing strategies, and large-scale graph workloads.

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