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Tier-1 employer and Hyderabad metro increase competition, but niche knowledge-graph specialization limits applicant pool.
Role requires specialist semantic, ontology, and knowledge-graph expertise, limiting cross-industry transferability.
Explicit 7+ years, mandatory data engineering and knowledge-graph skills create strict technical filters.
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Lead design and implementation of data pipelines to extract, transform, validate, and load context into an enterprise knowledge graph and semantic layer.
Build scalable, maintainable processes and enforce technical standards for knowledge graph population from multiple data sources ensuring data quality, lineage, versioning, and operational readiness.
Collaborate with ontology, architecture, platform teams to deliver reliable, current, and production-ready context for downstream applications, APIs, and AI/agentic systems.
7+ years experience in data engineering, pipeline engineering, or related technical architecture roles.
Strong expertise in building scalable data pipelines and integration workflows with familiarity in knowledge graphs, ontologies (OWL, RDF), semantic enrichment, and entity modeling.
Working knowledge of agentic AI, prompt engineering, and context engineering concepts.
Location: Hyderabad; Visa Sponsorship: No; Relocation: No; Travel Requirements: No Travel Required.
Experienced leader in data pipeline architecture with deep technical skills to translate semantic data requirements into implementable solutions compatible with enterprise semantic models.
Strong background in knowledge graph population, semantic ETL frameworks, and experience with tools/platforms like timbr or similar.
Proven ability to deliver robust, scalable, and operationally sound data pipelines supporting AI-enabled and context-driven applications in complex enterprise environments.