{"schemaVersion":"jobsearcher.job.v1","id":"c6a4e15b32d8fcbb4296b8cb","url":"https://jobsearcher.com/jobs/c6a4e15b32d8fcbb4296b8cb","canonicalUrl":"https://jobsearcher.com/jobs/c6a4e15b32d8fcbb4296b8cb","title":"US_East | Data Engineer_L3","description":"Job Description\n\nRole: Data and Ontology Engineer\r\nLocation: Boston, MA (Onsite)\r\nJob Overview\r\nWe are seeking an experiencedData and Ontology Engineerto support the development of an AI-driven Energy Intelligence for Services platform.\r\nThe client is building a solution that transforms existing condition-based maintenance (CBM) analytics into normalized equipment health scores, contextual diagnostics, and prioritized action plans for critical power infrastructure in data centers, initially focusing on Uninterruptible Power Supply (UPS) systems and busways.\r\nThe platform will be developed and validated using historical, previously diagnosed customer data rather than live system connections.\r\nA semantic layer, built on the client's existing ontology framework, will provide AI diagnostic agents with essential asset relationships, dependencies, connectivity, and site-level context.\r\nThe ideal candidate will have strong expertise in knowledge graphs, ontology development, semantic technologies, data integration, and entity resolution.\r\nKey Responsibilities:\r\n1. Data Preparation and Entity ResolutionCollect, organize, and prepare historical customer asset, alarm, and maintenance data for ontology development.\r\nPerform entity resolution to reconcile assets represented under different identifiers across multiple systems.\r\nDefine and implement identifier strategies, including IRIs, named graphs, and customer/site-level partitioning.\r\nIntegrate structured and semi-structured data from relational databases, spreadsheets, JSON, and other sources.\r\n2. Ontology Development and Semantic ModelingDevelop and extend ontologies using the client's existing ontology framework.\r\nModel asset types, power-chain relationships, and equipment dependencies.\r\nDefine relationships such as upstream/downstream connectivity, shared rooms, and common systems.\r\nEstablish semantic connections between assets and equipment health-score data.\r\nDevelop and maintain a consistent semantic vocabulary and shared business glossary.\r\n3. Data Validation and Quality AssuranceValidate semantic data models using SHACL.\r\nCreate actionable data-quality and conformance reports.\r\nIdentify and resolve data inconsistencies, mapping errors, and modeling issues.\r\nEnsure ontology structures accurately represent business and technical requirements.\r\n4. Collaboration and Domain Knowledge IntegrationConduct modeling workshops with service, reliability, and CBM subject matter experts.\r\nTranslate domain expertise into ontology definitions, relationships, and rules.\r\nCollaborate with software engineers to explain modeling decisions and implementation approaches.\r\nMaintain a shared glossary of domain terminology and asset relationships.\r\n5. Ontology Testing and CI/CDManage ontologies and mappings as version-controlled code.\r\nDevelop automated tests using SPARQL competency questions and SHACL conformance checks.\r\nIntegrate ontology validation and testing into continuous integration (CI) pipelines.\r\nMaintain competency questions and expected answers to evaluate AI diagnostic agents against historical incidents.\r\n6. Technical Documentation and ReportingCreate comprehensive ontology documentation, schema summaries, and sample SPARQL queries.\r\nProvide documentation supporting ontology-grounded AI diagnostic agents.\r\nCommunicate project progress, component status, and technical issues using Microsoft Office applications, including PowerPoint, Excel, Word, and Outlook.\r\nReview CAD and 2D component drawings, including Geometric Dimensioning and Tolerancing (GD&T).\r\nMandatory Skills and Qualifications:4+ years of experiencebuilding knowledge graphs, ontologies, or semantic data platforms used in production environments.\r\nStrong hands-on experience withRDF/RDFS, OWL 2, SPARQL 1.1, and SHACL .\r\nProficiency in Python, including libraries such as rdflib, pySHACL, or equivalent tools.\r\nExperience using Git, version control, and continuous integration pipelines.\r\nStrong experience preparing and reconciling complex, multi-source asset data.\r\nHands-on expertise in entity resolution and data integration across relational databases, spreadsheets, and JSON.\r\nExperience mapping source data into RDF using R2RML, RML, or equivalent ETL frameworks.\r\nAbility to collaborate with domain experts and translate business concepts into semantic data models.\r\nStrong communication skills and the ability to explain ontology modeling decisions to technical and nontechnical stakeholders.\r\nPreferred Qualifications:Experience with electrical power infrastructure and data-center equipment, including UPS, busways, switchgear, and Power Distribution Units (PDUs).\r\nFamiliarity with reliability engineering and maintenance concepts such as failure modes, Failure Mode and Effects Analysis (FMEA), and condition monitoring.\r\nKnowledge of industry-standard ontologies and vocabularies, including:Brick Schema\r\nRealEstateCore / ASHRAE 223\r\nSOSA/SSN\r\nQUDT\r\nISO 14224\r\nIEC 81346Experience integrating knowledge graphs with Large Language Models (LLMs), including GraphRAG, text-to-SPARQL, and ontology-grounded prompting.\r\nFamiliarity with property graph databases such as Neo4j and query languages such as Cypher.\r\nUnderstanding of the appropriate use cases for RDF-based knowledge graphs versus property graphs.\r\nExperience with ontology modeling tools such as Protégé, TopBraid, or LinkML in enterprise-scale environments.“All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.”","company":"Datum Software","rawCompany":"datum software","city":"Bloomfield","state":"NJ","isRemote":false,"isActive":false,"createdAt":"2026-11-03T02:02:51.256Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1243.00","title":"Database Architects","slug":"database-architects"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"518210","title":"Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services","slug":"computing-infrastructure-providers-data-processing-web-hosting-and-related-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"US_East | Data Engineer_L3","description":"Job Description\n\nRole: Data and Ontology Engineer\r\nLocation: Boston, MA (Onsite)\r\nJob Overview\r\nWe are seeking an experiencedData and Ontology Engineerto support the development of an AI-driven Energy Intelligence for Services platform.\r\nThe client is building a solution that transforms existing condition-based maintenance (CBM) analytics into normalized equipment health scores, contextual diagnostics, and prioritized action plans for critical power infrastructure in data centers, initially focusing on Uninterruptible Power Supply (UPS) systems and busways.\r\nThe platform will be developed and validated using historical, previously diagnosed customer data rather than live system connections.\r\nA semantic layer, built on the client's existing ontology framework, will provide AI diagnostic agents with essential asset relationships, dependencies, connectivity, and site-level context.\r\nThe ideal candidate will have strong expertise in knowledge graphs, ontology development, semantic technologies, data integration, and entity resolution.\r\nKey Responsibilities:\r\n1. Data Preparation and Entity ResolutionCollect, organize, and prepare historical customer asset, alarm, and maintenance data for ontology development.\r\nPerform entity resolution to reconcile assets represented under different identifiers across multiple systems.\r\nDefine and implement identifier strategies, including IRIs, named graphs, and customer/site-level partitioning.\r\nIntegrate structured and semi-structured data from relational databases, spreadsheets, JSON, and other sources.\r\n2. Ontology Development and Semantic ModelingDevelop and extend ontologies using the client's existing ontology framework.\r\nModel asset types, power-chain relationships, and equipment dependencies.\r\nDefine relationships such as upstream/downstream connectivity, shared rooms, and common systems.\r\nEstablish semantic connections between assets and equipment health-score data.\r\nDevelop and maintain a consistent semantic vocabulary and shared business glossary.\r\n3. Data Validation and Quality AssuranceValidate semantic data models using SHACL.\r\nCreate actionable data-quality and conformance reports.\r\nIdentify and resolve data inconsistencies, mapping errors, and modeling issues.\r\nEnsure ontology structures accurately represent business and technical requirements.\r\n4. Collaboration and Domain Knowledge IntegrationConduct modeling workshops with service, reliability, and CBM subject matter experts.\r\nTranslate domain expertise into ontology definitions, relationships, and rules.\r\nCollaborate with software engineers to explain modeling decisions and implementation approaches.\r\nMaintain a shared glossary of domain terminology and asset relationships.\r\n5. Ontology Testing and CI/CDManage ontologies and mappings as version-controlled code.\r\nDevelop automated tests using SPARQL competency questions and SHACL conformance checks.\r\nIntegrate ontology validation and testing into continuous integration (CI) pipelines.\r\nMaintain competency questions and expected answers to evaluate AI diagnostic agents against historical incidents.\r\n6. Technical Documentation and ReportingCreate comprehensive ontology documentation, schema summaries, and sample SPARQL queries.\r\nProvide documentation supporting ontology-grounded AI diagnostic agents.\r\nCommunicate project progress, component status, and technical issues using Microsoft Office applications, including PowerPoint, Excel, Word, and Outlook.\r\nReview CAD and 2D component drawings, including Geometric Dimensioning and Tolerancing (GD&T).\r\nMandatory Skills and Qualifications:4+ years of experiencebuilding knowledge graphs, ontologies, or semantic data platforms used in production environments.\r\nStrong hands-on experience withRDF/RDFS, OWL 2, SPARQL 1.1, and SHACL .\r\nProficiency in Python, including libraries such as rdflib, pySHACL, or equivalent tools.\r\nExperience using Git, version control, and continuous integration pipelines.\r\nStrong experience preparing and reconciling complex, multi-source asset data.\r\nHands-on expertise in entity resolution and data integration across relational databases, spreadsheets, and JSON.\r\nExperience mapping source data into RDF using R2RML, RML, or equivalent ETL frameworks.\r\nAbility to collaborate with domain experts and translate business concepts into semantic data models.\r\nStrong communication skills and the ability to explain ontology modeling decisions to technical and nontechnical stakeholders.\r\nPreferred Qualifications:Experience with electrical power infrastructure and data-center equipment, including UPS, busways, switchgear, and Power Distribution Units (PDUs).\r\nFamiliarity with reliability engineering and maintenance concepts such as failure modes, Failure Mode and Effects Analysis (FMEA), and condition monitoring.\r\nKnowledge of industry-standard ontologies and vocabularies, including:Brick Schema\r\nRealEstateCore / ASHRAE 223\r\nSOSA/SSN\r\nQUDT\r\nISO 14224\r\nIEC 81346Experience integrating knowledge graphs with Large Language Models (LLMs), including GraphRAG, text-to-SPARQL, and ontology-grounded prompting.\r\nFamiliarity with property graph databases such as Neo4j and query languages such as Cypher.\r\nUnderstanding of the appropriate use cases for RDF-based knowledge graphs versus property graphs.\r\nExperience with ontology modeling tools such as Protégé, TopBraid, or LinkML in enterprise-scale environments.“All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.”","datePosted":"2026-11-03T02:02:51.256Z","dateModified":"2026-11-03T02:02:51.256Z","hiringOrganization":{"@type":"Organization","name":"Datum Software","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Bloomfield","addressRegion":"NJ","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"c6a4e15b32d8fcbb4296b8cb"},"url":"https://jobsearcher.com/jobs/c6a4e15b32d8fcbb4296b8cb"}}