US_East | Data Engineer_L3

Job DescriptionJob DescriptionRole: Data and Ontology Engineer Location: Boston, MA (Onsite) Job Overview We are seeking an experienced Data and Ontology Engineer to support the development of an AI-driven Energy Intelligence for Services platform. The 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. The platform will be developed and validated using historical, previously diagnosed customer data rather than live system connections. A 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. The ideal candidate will have strong expertise in knowledge graphs, ontology development, semantic technologies, data integration, and entity resolution. Key Responsibilities: 1. Data Preparation and Entity ResolutionCollect, organize, and prepare historical customer asset, alarm, and maintenance data for ontology development.Perform entity resolution to reconcile assets represented under different identifiers across multiple systems.Define and implement identifier strategies, including IRIs, named graphs, and customer/site-level partitioning.Integrate structured and semi-structured data from relational databases, spreadsheets, JSON, and other sources.2. Ontology Development and Semantic ModelingDevelop and extend ontologies using the client's existing ontology framework.Model asset types, power-chain relationships, and equipment dependencies.Define relationships such as upstream/downstream connectivity, shared rooms, and common systems.Establish semantic connections between assets and equipment health-score data.Develop and maintain a consistent semantic vocabulary and shared business glossary.3. Data Validation and Quality AssuranceValidate semantic data models using SHACL.Create actionable data-quality and conformance reports.Identify and resolve data inconsistencies, mapping errors, and modeling issues.Ensure ontology structures accurately represent business and technical requirements.4. Collaboration and Domain Knowledge IntegrationConduct modeling workshops with service, reliability, and CBM subject matter experts.Translate domain expertise into ontology definitions, relationships, and rules.Collaborate with software engineers to explain modeling decisions and implementation approaches.Maintain a shared glossary of domain terminology and asset relationships.5. Ontology Testing and CI/CDManage ontologies and mappings as version-controlled code.Develop automated tests using SPARQL competency questions and SHACL conformance checks.Integrate ontology validation and testing into continuous integration (CI) pipelines.Maintain competency questions and expected answers to evaluate AI diagnostic agents against historical incidents.6. Technical Documentation and ReportingCreate comprehensive ontology documentation, schema summaries, and sample SPARQL queries.Provide documentation supporting ontology-grounded AI diagnostic agents.Communicate project progress, component status, and technical issues using Microsoft Office applications, including PowerPoint, Excel, Word, and Outlook.Review CAD and 2D component drawings, including Geometric Dimensioning and Tolerancing (GD&T).Mandatory Skills and Qualifications:4+ years of experience building knowledge graphs, ontologies, or semantic data platforms used in production environments.Strong hands-on experience with RDF/RDFS, OWL 2, SPARQL 1.1, and SHACL.Proficiency in Python, including libraries such as rdflib, pySHACL, or equivalent tools.Experience using Git, version control, and continuous integration pipelines.Strong experience preparing and reconciling complex, multi-source asset data.Hands-on expertise in entity resolution and data integration across relational databases, spreadsheets, and JSON.Experience mapping source data into RDF using R2RML, RML, or equivalent ETL frameworks.Ability to collaborate with domain experts and translate business concepts into semantic data models.Strong communication skills and the ability to explain ontology modeling decisions to technical and nontechnical stakeholders.Preferred Qualifications:Experience with electrical power infrastructure and data-center equipment, including UPS, busways, switchgear, and Power Distribution Units (PDUs).Familiarity with reliability engineering and maintenance concepts such as failure modes, Failure Mode and Effects Analysis (FMEA), and condition monitoring.Knowledge of industry-standard ontologies and vocabularies, including:Brick SchemaRealEstateCore / ASHRAE 223SOSA/SSNQUDTISO 14224IEC 81346Experience integrating knowledge graphs with Large Language Models (LLMs), including GraphRAG, text-to-SPARQL, and ontology-grounded prompting.Familiarity with property graph databases such as Neo4j and query languages such as Cypher.Understanding of the appropriate use cases for RDF-based knowledge graphs versus property graphs.Experience 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.”