Data and Ontology Engineer

Capgemini; Boston (MA); On-site; USD 58,000 - 90,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Retirement plan

Job summary

Tundra Technical Solutions is seeking a semantic data specialist to build and productionize knowledge graphs for an energy intelligence service focused on data centers.

You will shape ontologies, perform entity resolution across multi-source data, and ensure data quality with SHACL in a CI-driven workflow.

Qualifications

• 4+ years building knowledge graphs, ontologies or semantic data platforms used in production.
• Hands-on RDF/RDFS, OWL 2, SPARQL 1.1 and SHACL.
• Solid Python (rdflib, pySHACL or similar), git and CI.
• Ability to work with domain experts and explain modelling choices.

Responsibilities

• Harvest and organise the historical asset, alarm and maintenance data for ontology creation.
• Run entity resolution across multiple systems and define identifiers.
• Build the initial ontology: asset types, power-chain relationships, and links to health-score data.
• Validate data against the model with SHACL and produce data-quality reports.
• Run modelling sessions with service, reliability and CBM experts to turn knowledge into the model.

Skills

Knowledge graphs

Ontologies

Semantic data platforms

RDF/RDFS

OWL 2

SPARQL 1.1

SHACL

Python (rdflib)

Git

CI

Domain experts collaboration

Tools

RDF/RDFS

OWL 2

SPARQL 1.1

SHACL

Python (rdflib)

git

CI

Job description

Client is building Energy Intelligence for Services: an AI-driven service that turns existing condition-based maintenance (CBM) analytics into normalized health scores, contextual diagnostics and prioritized action plans for the critical power equipment of data centers, starting with UPS and busway.

The service is developed and validated on historical, already-diagnosed customer data rather than on live connections. A semantic layer - an asset and relationship model built on Company's existing ontology work - gives the diagnostic agents the context they need: which assets are connected, what depends on what, and where they sit on site.

Key Responsibilities:

Harvest and organise the customer's historical asset, alarm and maintenance data so it can be used for ontology creation.

Run entity resolution: reconcile the same asset appearing in several systems under several names, and define the identifier strategy (IRIs, named graphs, per-customer and per-site partitioning).

Build the initial ontology for this dataset on top of the existing client ontology: asset types, power-chain relationships (upstream / downstream, same room, same system) and the links to health-score data.

Validate the data against the model with SHACL and produce data-quality reports the team can act on.

Run modelling sessions with service, reliability and CBM experts; turn their knowledge into the model and a shared glossary.

Treat the ontology and mappings as code: versioned, tested (competency questions as SPARQL tests, SHACL conformance) and run in CI.

Write the documentation, schema summaries and example queries that ground the diagnostic agents.

Maintain the competency questions and expected answers used to test the agents on historical incidents.Clearly communicate component status and issues in MS-Office applications e.g. PowerPoint, Excel, Word, Outlook

Review CAD and 2D drawings for components including GD&T

Mandatory Skills:

4+ years building knowledge graphs, ontologies or semantic data platforms that were used in production.

Hands-on RDF/RDFS, OWL 2, SPARQL 1.1 and SHACL.

Solid Python (rdflib, pySHACL or similar), git and CI.

Ability to work with domain experts and to explain modelling choices to software engineers.

Data preparation and entity resolution on messy, multi-source asset data (relational, spreadsheets, JSON).

Mapping data to RDF (R2RML/RML or equivalent ETL).

Preferred Requirements:

Electrical power-chain or data-center domain knowledge (UPS, busway, switchgear, PDUs) and maintenance vocabulary (failure modes, FMEA, condition monitoring).

Standard vocabularies: Brick, RealEstateCore / ASHRAE 223, SOSA/SSN, QUDT, ISO 14224, IEC 81346.

Combining knowledge graphs with LLMs: GraphRAG, text-to-SPARQL, ontology-grounded prompting.

Property graphs (Neo4j/Cypher), with an informed view of when each approach fits.

The pay range that the employer in good faith reasonably expects to pay for this position is $41.61/hour - $65.01/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.

Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.