Lead Semantic Data Engineer

About the role

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.

What you'll do

• 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

What you need

• 6–10 years in data science & ai
• Machine learning
• SQL
• Python
• AWS
• ETL
• Terraform
• Any graduate

Good to have

• JIRA
• Git
• Docker
• CI/CD
• Product strategy
• Confluence