Sr. Graph Engineer

Apptad Inc; Arizona; On-site; USD 120,000 - 180,000; Full time

Job summary

Apptad Inc. is seeking a Senior Knowledge Graph Engineer to design, build, and scale the enterprise knowledge graph powering our search and AI products. You will own the path from ontology design through ingestion pipelines to GraphRAG applications that feed large language models with grounded context.

This is an onsite Phoenix role. You will collaborate with data scientists and product teams to model business concepts, ensure data quality, and implement scalable graph solutions using leading

Qualifications

• Bachelor's or Master's degree in CS, Info Science, Data Eng, or related field.
• 5+ years in data/software engineering with 3+ years building production knowledge graphs.
• Strong knowledge of RDF, OWL, SPARQL, SHACL.
• Hands-on experience with property graph databases and Cypher/Gremlin.
• Proficiency in Python, plus Java or Scala.
• Ontology modeling with Prot g/TopBraid/PoolParty.
• Experience with Spark, Kafka, Airflow, or dbt.
• Data modeling, schema design, and query optimization.
• Cloud platforms AWS/Azure/GCP.

Responsibilities

• Design and maintain ontologies, taxonomies, and semantic data models.
• Build and optimize knowledge graphs on Neo4j, Neptune, Stardog, GraphDB, or TigerGraph.
• Develop scalable ETL/ELT pipelines to ingest data into the graph.
• Implement entity resolution, linking, deduplication across sources.
• Extract entities and relationships from text using NLP/LLMs including NER and relation extraction.
• Build GraphRAG and hybrid retrieval systems with graph traversal, vector search, and LLMs.
• Write SPARQL, Cypher, and Gremlin queries for performance at scale.
• Enforce data quality and graph validation with SHACL and tests.
• Apply graph analytics and ML: centrality, community detection, link prediction, node embeddings, GNNs.
• Expose graph data via GraphQL, REST, SPARQL endpoints.
• Collaborate with data scientists, PMs, domain experts to model concepts.
• Set graph governance standards: versioning, provenance, lineage, access control.
• Mentor junior engineers and advocate semantic best practices.

Skills

Semantic web standards

Property graph databases

Python

Java/Scala

Ontology modeling

ETL/ELT pipelines

NLP/LLM extraction

APIs (GraphQL/REST/SPARQL)

Cloud platforms

Data modeling

Education

Bachelor's or Master's in CS/Info Science/Data Eng

Tools

Prot g

TopBraid

PoolParty

Neo4j

Cypher

Gremlin

SPARQL

SHACL

GraphDB

TigerGraph

Python

Java

Scala

Apache Spark

Kafka

Airflow

dbt

Job description

Role: Sr. Graph Engineer

Work Location: Phoenix, AZ (Onsite)

Job Type: Long term Contract

Job Description

We're looking for a Senior Knowledge Graph Engineer to design, build, and scale the enterprise knowledge graph behind our search, analytics, and AI products. You'll turn scattered data into connected knowledge. You'll own the path from ontology design through ingestion pipelines to GraphRAG applications that feed large language models (LLMs) with grounded context.

Key responsibilities

• Design and maintain ontologies, taxonomies, and semantic data models using RDF, RDFS, OWL, and SKOS.
• Build and optimize knowledge graphs on platforms such as Neo4j, Amazon Neptune, Stardog, GraphDB, or TigerGraph.
• Develop scalable ETL/ELT pipelines to ingest structured, semi-structured, and unstructured data into the graph.
• Implement entity resolution, entity linking, and deduplication across heterogeneous data sources.
• Extract entities and relationships from text using NLP and LLMs, including named entity recognition (NER), relation extraction, and schema-guided extraction.
• Build GraphRAG and hybrid retrieval systems that combine graph traversal, vector search, and LLMs.
• Write and tune complex SPARQL, Cypher, and Gremlin queries for performance at scale.
• Enforce data quality and graph validation with SHACL constraints and automated testing.
• Apply graph analytics and machine learning: centrality, community detection, link prediction, node embeddings, and graph neural networks (GNNs).
• Expose graph data through APIs such as GraphQL, REST, and SPARQL endpoints for downstream applications.
• Work with data scientists, product managers, and domain experts to model business concepts and use cases.
• Set graph governance standards, including versioning, provenance, lineage, and access control.
• Mentor junior engineers and advocate for semantic technology best practices.

Required qualifications

• Bachelor's or Master's degree in Computer Science, Information Science, Data Engineering, or a related field.
• 5+ years in data engineering or software engineering, with 3+ years building production knowledge graphs.
• Strong knowledge of semantic web standards: RDF, OWL, SPARQL, and SHACL.
• Hands‑on experience with property graph databases and Cypher or Gremlin.
• Proficiency in Python, plus Java or Scala.
• Ontology modeling experience with tools such as Prot g , TopBraid, or PoolParty.
• Experience with data pipelines using Apache Spark, Kafka, Airflow, or dbt.
• Solid grasp of data modeling, schema design, and query optimization.
• Experience with cloud platforms such as AWS, Azure, or GCP.

Preferred qualifications

• Experience with LLM frameworks such as LangChain or LlamaIndex, and with GraphRAG architectures.
• Familiarity with vector databases such as Pinecone, Weaviate, or pgvector, and with embedding models.
• Knowledge of graph ML libraries such as PyTorch Geometric, DGL, or Neo4j Graph Data Science.
• Exposure to industry ontologies such as FIBO, schema.org, SNOMED CT, or Gene Ontology.
• Experience with master data management (MDM) or data catalog such as Collibra or Alation.
• Neo4j Certified Professional or equivalent certification.
• Publications or open‑source contributions in semantic technologies.

Soft skills

• Able to turn ambiguous business questions into clear semantic models.
• Communicates well with both technical and non‑technical stakeholders.
• Thinks in systems and pays close attention to data quality.