Data Engineer (Knowledge Graph)

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., 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.

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. VeeRteq Solutions is an Equal Opportunity Employer