ontology engineer

<div><div>Job Title: Ontology Engineer</div>
Location: Remote 
Who We Are 
NWEA&reg; is a division of HMH that supports students and educators through research, assessment solutions, policy and advocacy services, professional learning and school improvement services that fight for equity, drive classroom impact and push for systemic change in our educational communities. For nearly 50 years, NWEA has developed innovative pre-K&ndash;12 assessments, including their flagship interim assessment, MAP&reg; Growth&trade; and their reading fluency and comprehension assessment, MAP&reg; Reading Fluency&trade;. 
What You&rsquo;ll Do 
The Ontology Engineer plays a pivotal role in shaping our organization's knowledge structure. They are responsible for developing and managing ontologies that underpin our information systems, facilitating better data organization and knowledge discovery. 
Responsibilities 
<ul><li>
Develops and maintains logical, semantically rich, and extensible ontologies that define and structure knowledge and underpin the K-12 education domain and HMH products.</li><li>
Supports integration between the ontology management system and source data management repositories, including triple store development and investigation by writing SPARQL queries against RDF, OWL or validating with SHACL.</li><li>
Ensures the accuracy and consistency of ontological structures by performing regular quality checks and updates as needed.</li><li>
Collaborates with cross-functional teams to understand their information needs and provide ontological solutions that support their objectives.</li><li>
Creates comprehensive documentation for ontologies and knowledge graphs, making them accessible to both technical and non-technical stakeholders.</li><li>
Coordinates with the Data engineering and operations, and content development teams to ensure the knowledge representation is aligned with enterprise data systems.</li></ul> 
What You&rsquo;ll Need 
<ul><li>Advanced degree in philosophy, computer science, information science, or quantitative field, or equivalent experience.</li><li>
Proficiency in ontology modeling languages such as RDF, OWL, or SHACL and vocabularies such as SKOS.</li><li>
Demonstrate advanced knowledge of ontology modeling languages such as RDF, OWL, or SHACL, and vocabularies like SKOS.</li><li>
Familiarity with query languages like SQL and SPARQL or other property graph query languages like Gremlin, openCypher.</li><li>
Experience with semantic data integration and using graph databases/triplestores, including modeling tradeoffs, query optimization, and operational considerations for production use.</li><li>
Experience with ontologies, knowledge graphs, and/or semantic technologies such as Stardog, Prot&eacute;g&eacute;, Semaphore, PoolParty, or related ontology management systems.</li><li>
Experience using foundational/top-level ontologies, especially Basic Formal Ontology (BFO), Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE), Common Core Ontologies, or OBO Foundry.</li><li>
Experience integrating multiple disparate data sets into a common vocabulary and writing effective queries for use in a production environment.</li><li>
Be skilled in integrating multiple disparate data sets into a common vocabulary and writing effective queries for production environments.</li><li>
Hands-on experience with high-level programming and scripting languages, such as Python, R, and Java.</li><li>
Strong software engineering practices applied to semantic and analytics assets: git, pull requests/code review, test/validation practices (e.g., SHACL/dbt tests where applicable), and CI/CD-aware release discipline.</li><li>
Excellent problem-solving skills and easily adapt to new technologies, trends, and frameworks.</li></ul> 
Salary range: 90k &ndash; 103k annually. 
Application Deadline:
The application window for this position is anticipated to close April 5, 2025. We encourage you to apply as soon as possible. The posting may be available after this date but is not guaranteed. 
HMH is fully committed to Equal Employment Opportunity and to attracting, retaining, developing and promoting the most qualified employees without regard to race, gender, color, religion, sexual orientation, family status, marital status, pregnancy, gender identity, ethnic/national origin, ancestry, age, disability, military status, genetic predisposition, citizenship status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran, other covered veteran, or any other characteristic protected by federal, state or local law. We are dedicated to providing a work environment free from discrimination and harassment, and where employees are treated with respect and dignity. We actively participate in E-Verify.
#LI-VA1</div>

Ontology Engineer

Information Technology Senior Management Forum

<div><div>Posted Date
3/18/2026
</div><div>Description
<div>At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We&rsquo;re looking for people who are determined to make life better for people around the world.
Position: Data Architect, Data Foundry
Location: San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN
Overview
Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.
Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation &amp; Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities&mdash;serving both human scientists and autonomous AI agents.
Position Summary
We are seeking Data Architects at multiple levels to design and build the data infrastructure that makes AI-native drug discovery possible. You will create the schemas, ontologies, data models, knowledge graphs, and platform architectures that transform raw scientific data into machine-actionable, FAIR-compliant, insight-ready assets&mdash;serving both discovery scientists and autonomous AI agents.
This role is the foundation of Architecture4Insight . Everything the software engineering team builds&mdash;pipelines, APIs, prototypes&mdash;depends on the data models and platform architecture this team designs. You will work with deep knowledge of scientific data (chemical, biological, HTE, automation-generated) to create custom-fit solutions, then partner with Tech@Lilly to scale and maintain them. The role spans three focus areas depending on expertise: data modeling &amp; ontologies , data platform &amp; lakehouse architecture , and knowledge graph &amp; specialized data systems .
Responsibilities
Data Modeling &amp; Ontologies
<ul><li>Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data that serve discovery workflows across the portfolio.</li>
<li>Define and maintain controlled vocabularies, metadata standards, and FAIR-compliant data frameworks in partnership with Preparedness4Insight.</li>
<li>Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.</li>
</ul>
Data Platform &amp; Lakehouse Architecture
<ul><li>Design and implement data lakehouse architecture using modern platforms (Databricks, Snowflake, or equivalent), including data storage patterns, partitioning strategies, and query optimization.</li>
<li>Build and optimize ETL/ELT pipelines using Spark, dbt, or similar tools to transform raw scientific data into analytical and ML-ready formats.</li>
<li>Implement real-time and streaming data integration (Kafka, Kinesis, event-driven patterns) connecting LIMS, instruments, and lab automation systems to the data infrastructure.</li>
</ul>
Knowledge Graph &amp; Specialized Data Systems
<ul><li>Design and implement knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) that capture molecular, target, pathway, and experimental relationships across the discovery landscape.</li>
<li>Architect specialized data solutions: array databases (TileDB) for genomics/imaging, document stores (MongoDB) for experimental records, and vector databases for embedding-based retrieval supporting ML and RAG workflows.</li>
<li>Build query and traversal patterns that enable scientists and AI agents to ask relational questions across the entire data landscape.</li>
</ul>
Cross-Functional Partnership
<ul><li>Partner with scientific software engineers to ensure data architectures are implementable, performant, and well-documented.</li>
<li>Collaborate with Methods4Insight to design data structures that support analytical model training, deployment, and evaluation.</li>
<li>Work with Tech@Lilly to define scaling strategies, ensure enterprise compliance, and transition data architectures to production-grade management.</li>
<li>Contribute to build-versus-buy-versus-adopt decisions by evaluating commercial and open-source data platforms against Data Foundry requirements.</li>
</ul>
Basic Requirements
<ul><li>B.S. or M.S. in Computer Science, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM field; Ph.D. valued for ontology and knowledge graph roles.</li>
<li>B.S. with 7+ years and M.S. with 5+ years of data architecture, data engineering, or scientific informatics' experience.</li>
<li>SQL skills and experience in multiple database paradigms (relational, graph, document, columnar, key-value).</li>
</ul>
Preferred Qualifications
<ul><li>Expertise in at least one of: data modeling/ontologies, data platform engineering (Databricks, Snowflake, Spark), or graph/specialized databases (Neo4j, Neptune, MongoDB).</li>
<li>Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data integration patterns.</li>
<li>Understanding of scientific data types and experimental workflows in life sciences or pharma (chemical, biological, HTE data).</li>
<li>Strong communication skills with ability to translate data architecture concepts for both technical and scientific audiences.</li>
<li>Pharmaceutical or biotech research industry experience, particularly in discovery data management or research informatics.</li>
<li>Experience with semantic web technologies: RDF, OWL, SPARQL, Prot&eacute;g&eacute;, or equivalent ontology engineering tools.</li>
<li>Hands-on experience with graph databases (Neo4j, Neptune, TigerGraph) and knowledge graph design patterns for scientific data.</li>
<li>Data lakehouse architecture experience: Databricks (Delta Lake, Unity Catalog), Snowflake, or equivalent; ETL/ELT with Spark, dbt.</li>
<li>Experience with streaming/real-time data platforms (Kafka, Kinesis, Flink) and event-driven architectures.</li>
<li>Familiarity with LIMS, ELN systems (e.g., Benchling), and laboratory instrument data integration.</li>
<li>Experience with vector databases (Pinecone, Weaviate, pgvector) and embedding-based retrieval for ML/RAG applications.</li>
<li>Array database experience (TileDB, Zarr) for genomics, imaging, or high-dimensional scientific data.</li>
<li>Experience with bioinformatics data formats (FASTA, BAM/CRAM, VCF) and biological sequence databases; familiarity with NGS data pipelines and proteomics data management.</li>
<li>FAIR data principles implementation experience and Data Readiness Level frameworks.</li>
<li>Scientific data standards and controlled vocabularies in chemistry (InChI, SMILES) or biology (Gene Ontology, UniProt, pathway databases such as Reactome or KEGG).</li>
</ul>
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation ) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women&rsquo;s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
Actual compensation will depend on a candidate&rsquo;s education, experience, skills, and geographic location. The anticipated wage for this position is
$132,000 - $193,600
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly&rsquo;s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
#WeAreLilly</div>
</div><div>Salary
132,000.00 - 193,600.00 Annual</div></div><div></div>

Data Architect, Data Foundry

Eli Lilly

<div>At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We&rsquo;re looking for people who are determined to make life better for people around the world.
Location: San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN<h2 class="jobSectionHeader">
Reports to: Lead, Data Architecture (R9), Architecture4Insight</h2><h1 class="jobSectionHeader">
Overview</h1>
Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.
Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation &amp; Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities&mdash;serving both human scientists and autonomous AI agents.<h1 class="jobSectionHeader">
Position Summary</h1>
We are seeking Data Architects at multiple levels to design and build the data infrastructure that makes AI-native drug discovery possible. You will create the schemas, ontologies, data models, knowledge graphs, and platform architectures that transform raw scientific data into machine-actionable, FAIR-compliant, insight-ready assets&mdash;serving both discovery scientists and autonomous AI agents.
This role is the foundation of Architecture4Insight. Everything the software engineering team builds&mdash;pipelines, APIs, prototypes&mdash;depends on the data models and platform architecture this team designs. You will work with deep knowledge of scientific data (chemical, biological, HTE, automation-generated) to create custom-fit solutions, then partner with Tech@Lilly to scale and maintain them. The role spans three focus areas depending on expertise: data modeling &amp; ontologies, data platform &amp; lakehouse architecture, and knowledge graph &amp; specialized data systems. You will independently design schemas, select technologies, and make build-vs-buy recommendations for their domain.<h1 class="jobSectionHeader">
Responsibilities</h1><h2 class="jobSectionHeader">
Data Modeling &amp; Ontologies</h2><ul><li>
Design and implement data models, schemas, and ontologies for chemical, biological, and automation-generated data that serve discovery workflows across the portfolio.</li><li>
Define and maintain controlled vocabularies, metadata standards, and FAIR-compliant data frameworks in partnership with Preparedness4Insight.</li><li>
Implement semantic data standards (RDF, OWL, SPARQL) and ontology engineering practices to create interoperable, machine-readable scientific data.</li></ul><h2 class="jobSectionHeader">
Data Platform &amp; Lakehouse Architecture</h2><ul><li>
Design and implement data lakehouse architecture using modern platforms (Databricks, Snowflake, or equivalent), including data storage patterns, partitioning strategies, and query optimization.</li><li>
Build and optimize ETL/ELT pipelines using Spark, dbt, or similar tools to transform raw scientific data into analytical and ML-ready formats.</li><li>
Implement real-time and streaming data integration (Kafka, Kinesis, event-driven patterns) connecting LIMS, instruments, and lab automation systems to the data infrastructure.</li></ul><h2 class="jobSectionHeader">
Knowledge Graph &amp; Specialized Data Systems</h2><ul><li>
Design and implement knowledge graphs (Neo4j, Amazon Neptune, TigerGraph) that capture molecular, target, pathway, and experimental relationships across the discovery landscape.</li><li>
Architect specialized data solutions: array databases (TileDB) for genomics/imaging, document stores (MongoDB) for experimental records, and vector databases for embedding-based retrieval supporting ML and RAG workflows.</li><li>
Build query and traversal patterns that enable scientists and AI agents to ask relational questions across the entire data landscape.</li></ul><h2 class="jobSectionHeader">
Cross-Functional Partnership</h2><ul><li>
Partner with scientific software engineers to ensure data architectures are implementable, performant, and well-documented.</li><li>
Collaborate with Methods4Insight to design data structures that support analytical model training, deployment, and evaluation.</li><li>
Work with Tech@Lilly to define scaling strategies, ensure enterprise compliance, and transition data architectures to production-grade management.</li><li>
Contribute to build-versus-buy-versus-adopt decisions by evaluating commercial and open-source data platforms against Data Foundry requirements.</li></ul><h1 class="jobSectionHeader">
Basic Requirements</h1><ul><li>
M.S. or Phd in Computer Science, Data Science, Bioinformatics, Computational Biology, Information Science, or related STEM field</li><li>
6&ndash;12+ years of data architecture, data engineering, or scientific informatics experience.</li><li>
Deep expertise in at least one of the focus areas: relational databases, data modeling and ontology engineering, data platform and lakehouse architecture (Databricks, Snowflake, Spark), or knowledge graph and specialized database systems (Neo4j, Neptune, MongoDB, TileDB)</li></ul><h1 class="jobSectionHeader">
Preferred Qualifications</h1><ul><li>
Working familiarity with multiple database paradigms &mdash; relational, graph, document, columnar, key-value &mdash; and strong SQL skills.</li><li>
Understanding of scientific data types and experimental workflows in life sciences or pharma (chemical, biological, HTE data).</li><li>
Strong communication skills with ability to translate data architecture concepts for both technical and scientific audiences.</li><li>
Familiarity with cloud platforms (AWS, Azure, or GCP) and modern data integration patterns.</li><li>
Pharmaceutical or biotech research industry experience, particularly in discovery data management or research informatics.</li><li>
Experience with semantic web technologies: RDF, OWL, SPARQL, Prot&eacute;g&eacute;, or equivalent ontology engineering tools.</li><li>
Hands-on experience with graph databases (Neo4j, Neptune, TigerGraph) and knowledge graph design patterns for scientific data.</li><li>
Data lakehouse architecture experience: Databricks (Delta Lake, Unity Catalog), Snowflake, or equivalent; ETL/ELT with Spark, dbt.</li><li>
Experience with streaming/real-time data platforms (Kafka, Kinesis, Flink) and event-driven architectures.</li><li>
Familiarity with LIMS, ELN systems (e.g., Benchling), and laboratory instrument data integration.</li><li>
Experience with vector databases (Pinecone, Weaviate, pgvector) and embedding-based retrieval for ML/RAG applications.</li><li>
Array database experience (TileDB, Zarr) for genomics, imaging, or high-dimensional scientific data.</li><li>
FAIR data principles implementation experience and Data Readiness Level frameworks.</li><li>
Scientific data standards and controlled vocabularies in chemistry (InChI, SMILES) or biology (Gene Ontology, UniProt).</li><li>
Experience with C, C++, or Rust for performance-critical data processing; familiarity with HPC data I/O patterns for large-scale scientific computations.</li></ul>
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status. 
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia Network, Black Employees at Lilly, Chinese Culture Network, Japanese International Leadership Network (JILN), Lilly India Network, Organization of Latinx at Lilly (OLA), PRIDE (LGBTQ+ Allies), Veterans Leadership Network (VLN), Women&rsquo;s Initiative for Leading at Lilly (WILL), enAble (for people with disabilities). Learn more about all of our groups.
Actual compensation will depend on a candidate&rsquo;s education, experience, skills, and geographic location. The anticipated wage for this position is
$151,500 - $222,200
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly&rsquo;s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
#WeAreLilly</div>

Advisor - Data Architect, Data Foundry

BitCot Technologies

We are seeking an experienced TopBraid EDG Implementation &amp; Support Engineer to lead the deployment, configuration, and ongoing support of TopQuadrant's TopBraid Enterprise Data Governance (EDG) platform within our healthcare organization. This role sits at the intersection of semantic technology, data governance, and healthcare informatics &mdash; helping us build a knowledge graph&ndash;driven data foundation that ensures data quality, regulatory compliance, and interoperability across our enterprise.You will be responsible for designing and managing ontologies, taxonomies, and business glossaries that power our clinical, operational, and research data governance programs. The ideal candidate brings hands-on experience with knowledge graph technologies and a passion for making complex data ecosystems navigable and trustworthy.Key ResponsibilitiesPlatform Implementation &amp; ConfigurationLead the installation, configuration, and upgrade lifecycle of TopBraid EDG across development, staging, and production environments.Configure EDG packages including Metadata Management, Vocabulary Management, Reference Data Management, and Business Glossaries to meet organizational needs.Integrate TopBraid EDG with existing healthcare IT systems (EHRs, data warehouses, clinical data repositories, master data management tools) using SPARQL, GraphQL, and REST APIs.Implement role-based access controls, governance workflows, and event-driven automation within the platform.Ontology &amp; Knowledge Graph DevelopmentDesign, build, and maintain healthcare-specific ontologies using SHACL (W3C standard) within TopBraid EDG's ontology editor.Customize and extend EDG's pre-built ontology models (EDG Schema &ndash; Base, Core, Data Assets, etc.) to align with organizational data models and healthcare standards (HL7 FHIR, SNOMED CT, ICD-10, LOINC, RxNorm).Develop and govern taxonomies, controlled vocabularies, and reference datasets to ensure consistent interpretation of clinical and business data.Model data lineage, provenance, and impact relationships across the enterprise knowledge graph.Data Governance &amp; Compliance SupportPartner with data stewards, compliance officers, and clinical informaticists to translate regulatory requirements (HIPAA, HITECH, CMS Interoperability rules) into enforceable governance policies within the platform.Configure data validation rules, quality checks, and automated enrichment processes to maintain data accuracy, completeness, and consistency.Support audit readiness by maintaining clear data lineage documentation and policy enforcement records.Ongoing Support &amp; OptimizationServe as the primary technical point of contact for TopBraid EDG troubleshooting, performance tuning, and issue resolution.Monitor platform health, manage server administration tasks, and coordinate with TopQuadrant support for advanced issues.Develop and deliver training sessions and documentation for data stewards, analysts, and business users on platform use and ontology concepts.Evaluate and implement new TopBraid features (e.g., EDG Copilots / AI assistants, AutoClassifier, Tagger modules) to accelerate governance maturity.Stakeholder CollaborationAct as a bridge between technical teams and business stakeholders, translating semantic modeling concepts into accessible language for non-technical audiences.Facilitate change management efforts to drive adoption of the ontology-first governance approach across the organization.Required QualificationsEducation: Bachelor's degree in Computer Science, Health Informatics, Biomedical Informatics, Information Science, or a related field. Master's degree preferred.Experience: 3&ndash;5+ years of hands-on experience implementing and supporting TopBraid EDG or comparable semantic/knowledge graph platforms.Semantic Web Expertise: Strong working knowledge of RDF, OWL, SHACL, SKOS, SPARQL, and knowledge graph concepts.Ontology Development: Demonstrated experience designing and managing ontologies, taxonomies, and controlled vocabularies in an enterprise setting.Healthcare Domain Knowledge: Familiarity with healthcare data standards such as HL7 FHIR, SNOMED CT, ICD-10, LOINC, or similar clinical terminologies.Integration Skills: Experience with GraphQL, REST APIs, and connecting governance platforms to enterprise data systems.Data Governance: Understanding of data governance frameworks, metadata management, data lineage, and data quality principles.Preferred QualificationsExperience with TopBraid EDG administration, including server configuration, product configuration parameters, and workspace management.Familiarity with healthcare regulatory requirements (HIPAA, HITECH, 21st Century Cures Act, CMS Interoperability and Patient Access rules).Experience with AI/ML-assisted data classification, auto-tagging, or NLP in a governance context.Knowledge of graph databases (e.g., Neptune, Neo4j, Stardog) and how they complement RDF-based knowledge graphs.Experience with healthcare data platforms (Epic, Cerner, clinical data lakes, OMOP CDM).Relevant certifications such as CDMP (Certified Data Management Professional) or W3C semantic web credentials.Technical EnvironmentPlatform: TopBraid EDG (Metadata Management, Vocabulary Management, Reference Data Management)Standards: SHACL, RDF, OWL, SKOS, SPARQL, GraphQLHealthcare Standards: HL7 FHIR, SNOMED CT, ICD-10, LOINC, RxNormIntegration: REST APIs, GraphQL endpoints, ETL/ELT pipelinesInfrastructure: [Cloud provider, e.g., AWS / Azure / GCP] or on-premises deploymentWhat We OfferCompetitive salary and comprehensive benefits packageOpportunity to shape the semantic data infrastructure of a leading healthcare organizationProfessional development budget for conferences, certifications, and trainingCollaborative, mission-driven team focused on improving patient outcomes through better dataFlexible work arrangementsJob Types: Full-time, ContractPay: $75,000.00 - $190,000.00 per yearWork Location: Remote

TopBraid EDG Engineer

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