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Tier-1 brand and metro location increase competition, but specialized knowledge graph skillset narrows applicant pool.
Requires specialized semantic and knowledge-graph experience, limiting cross-industry transferability.
Explicit 7+ years requirement and mandatory knowledge-graph and pipeline expertise raise filtering strictness.
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Lead design and implementation of pipelines to ingest, transform, validate, and load enterprise data context into knowledge graphs and semantic layers.
Establish scalable, maintainable processes and technical standards for knowledge graph population, semantic enrichment, and data validation supporting batch and near-real-time needs.
Collaborate with cross-functional teams to ensure pipeline outputs align with semantic and enterprise context models, enabling reliable agentic AI and downstream applications.
7+ years experience in data engineering, pipeline engineering, or related platform roles.
Strong expertise in building scalable data pipelines and integration workflows.
Familiarity with knowledge graphs, ontologies, RDF, OWL, semantic enrichment, entity modeling, and data quality practices.
Knowledge of agentic AI, prompt engineering, and context engineering concepts.
Experienced in designing and operating semantic data pipelines feeding enterprise knowledge graphs with strong technical leadership.
Comfortable working closely with ontology teams, architects, and engineers to translate complex semantic requirements into robust production systems.
Skilled in applying semantic technologies, data governance, and AI-contextual integration to support scalable, production-ready data context layers.