Senior Knowledge Graph Developer

United Airlines•Chicago, IL

6d•$112,480 - $146,540

About The Position

We are seeking an experienced and passionate Senior Knowledge Graph Developer to design, build, and scale our enterprise knowledge graph initiatives. In this role, you will be the technical lead responsible for transforming complex, siloed data into an interconnected, semantic knowledge layer. You will bridge the gap between data engineering, ontology design, and AI/ML applications, ensuring our knowledge graphs are performant, governed, and instrumental in powering our next-generation AI and analytics solutions.

Requirements

6–10+ years of software engineering or data architecture experience, with at least 4+ years dedicated to knowledge graph engineering, graph database development, or semantic web technologies.
Proven hands-on experience with at least one major graph platform (e.g., Neo4j, AWS Neptune, Stardog, TigerGraph, GraphDB).
Deep proficiency in at least one graph query language (Cypher, SPARQL, or Gremlin).
Strong understanding of RDF, OWL, RDFS, SKOS, and linked data principles.
Advanced proficiency in Python or Java for data processing, graph ingestion, and API development.
Experience with or strong interest in graph-augmented AI, vector search, RAG, and large language model integration patterns.
Experience deploying and managing graph solutions in cloud environments (AWS, GCP, or Azure) using containerized workflows (Docker/Kubernetes).

Nice To Haves

Experience with ontology design tools like Protégé.
Knowledge of semantic validation frameworks such as SHACL or ShEx.
Experience working in a regulated industry with strict data governance, lineage, and PII requirements.
Proven track record of moving graph-based projects from PoC to production-scale enterprise capabilities.

Responsibilities

Design and implement scalable, enterprise-grade knowledge graph schemas using property graph (e.g., Neo4j, TigerGraph) and/or RDF-based models.
Define and maintain formal ontologies, taxonomies, and semantic models that standardize business meaning and entity relationships across domains.
Lead the development of robust ETL/ELT pipelines to ingest, transform, and map heterogeneous data sources (RDBMS, NoSQL, APIs, unstructured text) into the knowledge graph.
Develop, optimize, and maintain complex graph queries (Cypher, SPARQL, Gremlin). Design stable, high-performance APIs to expose graph data for downstream AI services and applications.
Collaborate with data science teams to integrate the knowledge graph with LLMs, RAG (Retrieval-Augmented Generation) pipelines, graph embeddings, and other agentic frameworks to enhance reasoning and explainability.
Establish best practices for graph data quality, lineage, access control, and metadata management. Monitor graph performance and optimize indexing and traversal strategies for production environments.
Act as a subject matter expert and mentor for junior engineers. Drive adoption of graph technologies through documentation, code reviews, and community-of-practice contributions.