Senior Graph Engineer (Python)

United States·Palo Altosenior

Other

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Quick Summary

Key Responsibilities

Graph Architecture and Modeling: Design and build scalable knowledge graphs, defining schemas, ontologies, entities, and relationships that reflect complex business domains.

Requirements Summary

Experience: 6+ years of professional software engineering experience, with strong expertise in Python. 2+ years of hands-on experience developing graph-based applications, knowledge graphs,

Technical Tools

Other

Responsibilities

~1 min read

→Graph Architecture and Modeling: Design and build scalable knowledge graphs, defining schemas, ontologies, entities, and relationships that reflect complex business domains.
→Data Integration: Build pipelines that extract, normalize, and connect data from documents, databases, and external systems, with support for incremental updates.
→Entity Resolution and Data Quality: Implement entity matching, deduplication, relationship validation, and provenance tracking to maintain trustworthy graph data.
→Graph Retrieval and AI Integration: Develop graph-based retrieval capabilities and integrate them with vector search, RAG systems, and AI agents.
→Performance and Reliability: Optimize graph queries, indexing, and storage; implement testing, monitoring, access controls, and recovery practices.
→Python Development: Develop production-grade services, APIs, and pipelines for graph ingestion, querying, and integration with enterprise applications.

Requirements

~1 min read

6+ years of professional software engineering experience, with strong expertise in Python.
2+ years of hands-on experience developing graph-based applications, knowledge graphs, or graph data platforms

Ideally, experience using knowledge graphs in AI/agentic applications to support graph or hybrid Graph-RAG retrieval

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Advanced proficiency in Python and libraries like NetworkX, Pandas, NumPy, or similar.
Strong understanding of graph data modeling, graph traversal, indexing, and query optimization.
Hands-on experience with a graph database such as Neo4j, Amazon Neptune, or Azure Cosmos DB for Apache Gremlin.
Proficiency in a graph query language such as Cypher, Gremlin, or SPARQL, with an understanding of property graph or RDF models.
Experience building data ingestion and transformation pipelines across structured and unstructured sources.
Familiarity with entity resolution, ontology design, data lineage, and graph data quality practices.
Familiarity with knowledge graphs in AI applications, including GraphRAG, hybrid graph and vector retrieval, or agent tools, is a plus.

Excellent communication skills, including the ability to explain graph concepts and technical tradeoffs to nontechnical stakeholders.
Strong analytical and problem-solving skills, with attention to data quality and system reliability.
Ability to own projects from design through deployment and collaborate across disciplines.
Comfortable navigating ambiguity and evolving priorities in a fast-paced startup.