Semantic Data Engineer

About the role

Merck is looking for a semantic data engineer to build and scale enterprise-grade knowledge graph solutions that support advanced analytics and decision-making in pharmaceutical research. The role designs semantic data layers that integrate heterogeneous scientific datasets, enabling end-to-end traceability, cross-domain insights and AI/ML-driven discovery. It combines knowledge graphs, ontology engineering and data engineering, with strong awareness of pharmaceutical R&D.

Key responsibilities

• Design and implement scalable knowledge graph architectures integrating multi-modal data (omics, process, analytical, lineage)
• Build and manage graph data models for entities such as samples, processes, assays, targets and biological relationships
• Drive adoption of graph technologies such as RDF, property graphs and SPARQL/Cypher
• Work with platform teams on scalable infrastructure such as Amazon Neptune and serverless pipelines
• Ensure performance, scalability and governance of knowledge graph platforms
• Collaborate with product managers, scientists, data engineers and AI/ML teams to turn business needs into semantic models

Required profile

• Semantic web technology stack (RDF, SPARQL)
• AWS, especially serverless services (Lambda, Step Functions)
• Data engineering (SQL, ETL) and Python programming
• DevSecOps practices: CI/CD, Git, infrastructure-as-code (CloudFormation, Terraform) and containers (Docker)
• Desired: cheminformatics and pharmaceutical research exposure, AI/ML applications using graph data, Atlassian tools (Jira, Confluence), good communication and a growth mindset

What we offer

• Regular employee status
• Visa sponsorship available
• Hybrid work arrangement
• No relocation required