Data Platform Engineer
Full Time
full-time
5 Nov 2025
Verified by Turrior
Content + Source + Freshness • 17 Dec 2025 • 95% confidence
76 / 100
Offer value
Moderate score due to a strong skill focus on knowledge modeling, which is very specialized, and prospects for working with AI-related technologies.
- Focus on innovative knowledge modeling and ontology design.
- Collaboration opportunities across multiple domains.
- Requires specific expertise in graph database technologies.
Pros
- Involvement in innovative graph and semantic modeling projects.
- Strong emphasis on domain knowledge integration.
- Potential for impact on AI-driven recommendations.
Cons
- High specialization may deter generalists.
- Limited information on salary structure.
- Remote work opportunities might be constrained.
Who it's for
Mid to Senior • On-site
Good fit
- Data engineers with a strong interest in knowledge modeling
- Professionals looking to work on AI-driven projects
- Innovators who thrive on complex data challenges
Not recommended for
- Generalists in data engineering without specific expertise
- Candidates seeking fully remote roles
- Less experienced applicants
Motivation fit
Interest in driving data strategies through knowledge frameworksDesire to work on sophisticated data projectsEnthusiasm for collaborating with diverse teams
Key skills
Ontology developmentKnowledge graph managementCollaboration within multi-disciplinary teamsSemantic model creation
Score: 76/100 AI verified analysis
About the job
Project Role : Data Platform Engineer
Project Role Description : Assists with the data platform blueprint and design, encompassing the relevant data platform components. Collaborates with the Integration Architects and Data Architects to ensure cohesive integration between systems and data models.
Must have skills : Data Modeling Techniques and Methodologies
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: As a Data Platform Engineer, you will assist with the data platform blueprint and design, encompassing the relevant data platform components. Your typical day will involve collaborating with Integration Architects and Data Architects to ensure cohesive integration between systems and data models, while also engaging in discussions to refine and enhance the overall data architecture. You will be involved in various stages of the data platform lifecycle, ensuring that all components work harmoniously to support the organization's data needs and objectives. Key Responsibilities: - Knowledge Modeling & Ontology Design - Knowledge in any of the domain-specific standards - Develop ontologies and taxonomies using any of the open-source knowledge graph tools and build sophisticated knowledge graphs for structured data representation that aligns with domain standards. - Build and refine knowledge graphs for structured data representation, aligning with domain standards - Design scalable architectures for linked data and semantic models, integrating data from multiple sources. - Design metadata schemas and common data vocabulary, leveraging RDF/OWL to enhance data accessibility. - Use tools like Protégé or TopBraid Composer or any other tool to define and manage ontology structures. - Develop data models that support semantic search, data extraction, and AI-driven recommendations. Technical Experience: Must Have Skills: - Minimum of 2 years in ontology development, knowledge modeling, and graph database management. - Proficiency in RDF, OWL, SKOS or SHACL - Proficiency in Protégé or TopBraid Composer or any other modelling tool - Familiarity with SPARQL or other graph query languages - Knowledge in any of the knowledge graph platforms like Neo4j, Dgraph, StarDog, TopBriad ,ArangoDB, and Blazegraph. - Ability to incorporate domain knowledge into semantic models for actionable business insights. Good to Have Skills: - Collaborate with AI/ML teams to implement natural language processing and contextual data retrieval using knowledge graphs. - Enhance data discovery and search capabilities through graph-based search relevancy and knowledge representation. - Experience with knowledge graph visualization tools like Graphistry and Gephi. - Experience in programming skills in Python or Java to implement custom graph applications and integrations. - Integrate graph database solutions for efficient data querying and management. - Familiarity with open-source graph databases and their applications in real-time analytics. Professional Experience: - Good communication skills for conveying complex concepts to technical and non-technical stakeholders. - Ability to work both independently and within a team setting, showing leadership in best practices. - Proactive, innovative, and detail-oriented, with a strong focus on emerging technologies. Educational Qualification: - Bachelor’s or master’s degree in information science, Data Science, Knowledge Management, or a related field. - Certifications in ontology management or data standards are highly valued.
Project Role Description : Assists with the data platform blueprint and design, encompassing the relevant data platform components. Collaborates with the Integration Architects and Data Architects to ensure cohesive integration between systems and data models.
Must have skills : Data Modeling Techniques and Methodologies
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary: As a Data Platform Engineer, you will assist with the data platform blueprint and design, encompassing the relevant data platform components. Your typical day will involve collaborating with Integration Architects and Data Architects to ensure cohesive integration between systems and data models, while also engaging in discussions to refine and enhance the overall data architecture. You will be involved in various stages of the data platform lifecycle, ensuring that all components work harmoniously to support the organization's data needs and objectives. Key Responsibilities: - Knowledge Modeling & Ontology Design - Knowledge in any of the domain-specific standards - Develop ontologies and taxonomies using any of the open-source knowledge graph tools and build sophisticated knowledge graphs for structured data representation that aligns with domain standards. - Build and refine knowledge graphs for structured data representation, aligning with domain standards - Design scalable architectures for linked data and semantic models, integrating data from multiple sources. - Design metadata schemas and common data vocabulary, leveraging RDF/OWL to enhance data accessibility. - Use tools like Protégé or TopBraid Composer or any other tool to define and manage ontology structures. - Develop data models that support semantic search, data extraction, and AI-driven recommendations. Technical Experience: Must Have Skills: - Minimum of 2 years in ontology development, knowledge modeling, and graph database management. - Proficiency in RDF, OWL, SKOS or SHACL - Proficiency in Protégé or TopBraid Composer or any other modelling tool - Familiarity with SPARQL or other graph query languages - Knowledge in any of the knowledge graph platforms like Neo4j, Dgraph, StarDog, TopBriad ,ArangoDB, and Blazegraph. - Ability to incorporate domain knowledge into semantic models for actionable business insights. Good to Have Skills: - Collaborate with AI/ML teams to implement natural language processing and contextual data retrieval using knowledge graphs. - Enhance data discovery and search capabilities through graph-based search relevancy and knowledge representation. - Experience with knowledge graph visualization tools like Graphistry and Gephi. - Experience in programming skills in Python or Java to implement custom graph applications and integrations. - Integrate graph database solutions for efficient data querying and management. - Familiarity with open-source graph databases and their applications in real-time analytics. Professional Experience: - Good communication skills for conveying complex concepts to technical and non-technical stakeholders. - Ability to work both independently and within a team setting, showing leadership in best practices. - Proactive, innovative, and detail-oriented, with a strong focus on emerging technologies. Educational Qualification: - Bachelor’s or master’s degree in information science, Data Science, Knowledge Management, or a related field. - Certifications in ontology management or data standards are highly valued.
