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Strong employer and metro raise competition, but niche graph/semantic specialization limits applicant pool.
High because knowledge graph, ontology, and semantic engineering skills are highly domain-specific.
High due to explicit 5+ years and mandatory graph, semantic, and AWS technology requirements.
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Design, build, and maintain graph-based data structures and knowledge graph solutions using AWS and TIMBR-like platforms to power enterprise context services.
Model and optimize graph entities, relationships, and semantic structures for consistent semantic context across enterprise data products that support AI/agentic applications.
Ensure graph data pipelines, validation, performance, and APIs are production-ready, governed, and enable reuse by downstream applications and intelligent agents.
Minimum 5 years experience in data engineering, graph engineering, semantic engineering, or related roles.
Strong hands-on experience with AWS cloud technologies.
Experience with graph databases, knowledge graphs, semantic layers, or ontology-driven models; familiarity with TIMBR or similar ontology-driven data access platforms.
Work Experience Required: 5+ years as explicitly mentioned in the JD.
Experienced graph engineer with demonstrated ability to model and operationalize scalable enterprise semantic layers and knowledge graphs on cloud-native platforms.
Strong domain fit with hands-on knowledge of AWS services (e.g., S3, Glue, Neptune) and TIMBR-like ontology platforms supporting AI and context-driven applications.
Comfortable collaborating with ontology modelers, architects, and engineers to deliver governed, consistent graph context that supports enterprise-scale AI/agentic systems.