Graph Engineer, Data Context Layer

Merck Merck · Pharma · Telangana, India

Seeking a Graph Engineer to design, build, and maintain the graph and semantic infrastructure for a Data Context Layer (DCL) that supports enterprise data products and agentic AI capabilities. The role involves modeling, populating, optimizing, and operationalizing graph-based data structures, with a focus on AWS and TIMBR-like tools.

What you'd actually do

  1. Design and implement graph-based data structures and knowledge graph solutions in AWS and/or TIMBR-like platforms.
  2. Model entities, relationships, and semantic structures that represent enterprise context consistently across domains.
  3. Build and maintain pipelines that ingest, transform, enrich, and populate graph data from source systems.
  4. Collaborate with ontology modelers, data engineers, architects, and platform teams to ensure graph models align with enterprise standards.
  5. Optimize graph performance, query patterns, and data access for downstream applications and APIs.

Skills

Required

  • 5+ years of experience in data engineering, graph engineering, semantic engineering, or related technical roles.
  • Strong hands-on experience with AWS cloud technologies.
  • Experience working with graph databases, knowledge graphs, semantic layers, or ontology-driven models.
  • Familiarity with TIMBR or TIMBR-like tools or similar ontology-driven data access platforms.
  • Strong understanding of graph modeling, entity relationships, and semantic data structures.
  • Experience building scalable, production-ready data or graph pipelines.
  • Familiarity with agentic AI, prompt engineering, and context engineering concepts.
  • Ability to collaborate with engineers, architects, and domain experts.

Nice to have

  • Experience with AWS services such as S3, Glue, Lambda, Step Functions, DynamoDB, Neptune, Redshift, Athena, or CloudWatch.
  • Familiarity with RDF, OWL, SPARQL, knowledge graphs, or ontology management tools.
  • Experience with graph query optimization, data validation, and lifecycle management.
  • Background in metadata management, master data, or enterprise semantic platforms.
  • Experience supporting AI-enabled or context-driven applications.
  • Familiarity with CI/CD, observability, and secure production operations.

What the JD emphasized

  • Data Context Layer (DCL)
  • agentic AI capabilities
  • AWS cloud technologies
  • TIMBR-like tools
  • ontologies, OWL, RDF, agentic AI, prompt engineering, and context engineering

Other signals

  • Data Context Layer (DCL)
  • agentic AI capabilities
  • knowledge graphs
  • semantic technologies
  • AWS cloud technologies