Data Engineer (Knowledge Graph)

Job Title: Data Engineer (Knowledge Graph)

Location: [Remote USA]

Position Overview

We are seeking a skilled Data Engineer with expertise in Knowledge Graphs to design, build, and maintain scalable data pipelines and graph databases. In this role, you will bridge traditional data engineering (ETL/ELT, data modeling, warehouse architecture) with graph technologies to turn complex, interconnected datasets into actionable semantic insights.

Key Responsibilities

• Graph & Data Modeling: Design and implement graph data models (Property Graph / RDF) to represent complex business domains, entities, and relationships.

• Pipeline Development: Build, optimize, and maintain robust ETL/ELT pipelines to ingest, transform, and map structured and unstructured data into graph storage systems.

• Graph Architecture: Manage, query, and optimize graph database solutions (e.g., Neo4j, Amazon Neptune, GraphDB, TigerGraph) using query languages like Cypher, SPARQL, or Gremlin.

• Data Integration & Quality: Implement entity resolution, link prediction, and data deduplication techniques to maintain high-quality semantic data integration.

• API & Service Integration: Expose graph data through RESTful or GraphQL APIs for downstream analytics, recommendation systems, search engines, and AI/ML pipelines (including RAG implementations).

• Cross-Functional Collaboration: Partner with Data Scientists, Software Engineers, and Product Managers to define requirements and deliver graph-powered tools.

Qualifications & Requirements

• Education: Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related technical field.

• Data Engineering Experience: 8+ years of core data engineering experience working with SQL, Python/Scala, big data frameworks (Apache Spark), and cloud platforms (AWS, GCP, or Azure).

• Graph Technologies: Hands-on experience with graph databases (e.g., Neo4j, Neptune, Stardog, AllegroGraph) and graph query languages (Cypher, SPARQL, or Gremlin).

• Ontology & Semantics: Understanding of Semantic Web standards (OWL, RDF, SHACL, SKOS) or property graph principles.

• Data Warehousing & Orchestration: Familiarity with modern data tools like Snowflake, Databricks, dbt, and workflow schedulers like Apache Airflow.

Preferred Skills

• Experience with Graph Neural Networks (GNNs) or graph analytics algorithms (e.g., PageRank, Community Detection, Shortest Path).

• Exposure to Retrieval-Augmented Generation (RAG) architecture using GraphRAG techniques for LLM applications.

• Knowledge of vector databases and embedding generation alongside semantic graphs.