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Tier-1 brand but specialized, senior knowledge-graph role reduces candidate density.
Highly specialized semantic graph and pharma domain knowledge limits cross-industry transferability.
Mandatory niche graph, AWS/IaC skills and pharma governance increase filtering.
Lead design and implementation of scalable knowledge graph architectures integrating diverse scientific datasets in pharmaceutical research.
Drive adoption and governance of graph technologies and platforms such as RDF, SPARQL, Amazon Neptune within enterprise-scale environments.
Collaborate with cross-functional teams to translate business needs into semantic data models and mentor engineers on ontology and graph engineering best practices.
Experience with semantic web technologies including RDF and SPARQL.
Proficiency in Amazon Web Services, especially serverless services like Lambda and Step Functions.
Strong data engineering skills including SQL, ETL, and programming in Python.
Work Experience Required: Not explicitly mentioned in the JD.
Deep expertise in knowledge graph architecture, ontology engineering, and graph database technologies within pharmaceutical R&D context.
Experience with DevSecOps practices including CI/CD, Git, infrastructure-as-code (CloudFormation/Terraform), and containerization (Docker).
Ability to lead cross-team collaboration and mentor engineering teams in semantic data engineering and graph technology adoption.