Lead Semantic Data Engineer

Job Description

Our company is seeking a lead semantic data engineer to architect, build, and scale enterprise-grade knowledge graph solutions that power advanced analytics and decision-making in pharmaceutical research. This role will lead the design of semantic data layers that integrate heterogeneous scientific datasets, enabling end-to-end traceability, cross-domain insights, and AI/ML-driven discovery. The ideal candidate will combine deep expertise in knowledge graphs, ontology engineering, and data engineering, with strong domain awareness in pharmaceutical R&D.

• →

  Design and implement scalable knowledge graph architectures to integrate multi-modal data (e.g., omics, process, analytical, lineage)

• →

  Build and manage graph data models representing entities such as samples, processes, assays, targets, and biological relationships

• →

  Drive adoption of graph technologies (e.g., RDF, property graphs, SPARQL/Cypher)

• →

  Partner with platform teams to implement scalable infrastructure (e.g., Amazon Neptune, serverless pipelines)

• →

  Ensure performance, scalability, and governance of knowledge graph platforms

• →

  Collaborate with product managers, scientists, data engineers, and AI/ML teams to translate business needs into semantic models

• →

  Define and execute the knowledge graph roadmap aligned with enterprise data product strategy

• →

  Mentor engineers and establish best practices in ontology development, modeling, and graph engineering

• →

  Drive adoption of knowledge graph capabilities across use cases and programs

• Semantic web technology stack (e.g., RDF data format, SPARQL query language)

• Amazon Web Services, particularly the serverless services (e.g., Lambda, Step Functions)

• Data engineering (e.g., SQL queries, ETL jobs)

• Programming in Python

• Experience with DevSecOps practices like continuous integration (CI) and continuous delivery (CD), source code version control (e.g., Git), infrastructure-as-code (e.g., CloudFormation, Terraform), and containers (e.g., Docker)

• Cheminformatics and familiarity with the pharmaceutical research

• Exposure to AI/ML applications leveraging graph data

• Good communication and collaboration skills, and ability to collaborate with other teams to develop solutions

• Experience with Atlassian stack of tools for agile software development (e.g., Jira, Confluence)

• Demonstrate growth mindset and ability to work with enterprise teams

Current Employees apply HERE

Current Contingent Workers apply HERE