Anblicks
Key Responsibilities
Ontology & Knowledge Graph Engineering
Design and Modeling : Lead the design and implementation of formal ontologies and semantic models to accurately represent complex business entities and their relationships.
KG Implementation : Execute the build-out and continuous enrichment of the Customer Knowledge Graph, integrating data from from Different Data systems, transaction databases, marketing platforms, and other customer touchpoints.
Relationship Discovery : Implement algorithms and analytical processes to detect hidden relationships such as corporate family structures, shared personnel, common addresses, buying groups, and influence networks.
Graph Query Development : Write optimized queries against the graph database to support relationship analysis, pattern detection, and feature extraction for downstream applications.
Platform Operations : Monitor, maintain, and tune the performance and scalability of the graph database to ensure high availability and efficient data access.
Required Qualifications and Skills
Experience
8+ years of hands-on technical experience in Data Engineering, Software Development, or Analytics.
2+ years dedicated hands-on experience in Knowledge Graph development and relationship mapping, preferably with
customer or client data . Technical Skills Knowledge Graph/Ontology : Deep practical expertise in graph data modeling, ontology development, and semantic modeling principles (RDF, RDFS, OWL, SHACL). Graph Databases : Proven hands-on experience with at least one major Graph Database technology such as Neo4j, AWS Neptune, TigerGraph, or JanusGraph, and expertise in native query languages (Cypher, SPARQL, or Gremlin). Graph Analytics : Strong experience with graph algorithms including community detection, centrality measures, path finding, pattern matching, and relationship scoring. Programming : Strong proficiency in Python for data manipulation, graph algorithm implementation, and data transformations. Data Engineering : Solid experience building scalable data pipelines using modern tools such as Apache Spark, Kafka, Airflow, or dbt. Customer Data : Understanding of customer master data management, and entity resolution techniques. Cloud & DevOps : Experience with version control (Git) and familiarity with CI/CD processes and major cloud platforms (AWS, Azure).
customer or client data . Technical Skills Knowledge Graph/Ontology : Deep practical expertise in graph data modeling, ontology development, and semantic modeling principles (RDF, RDFS, OWL, SHACL). Graph Databases : Proven hands-on experience with at least one major Graph Database technology such as Neo4j, AWS Neptune, TigerGraph, or JanusGraph, and expertise in native query languages (Cypher, SPARQL, or Gremlin). Graph Analytics : Strong experience with graph algorithms including community detection, centrality measures, path finding, pattern matching, and relationship scoring. Programming : Strong proficiency in Python for data manipulation, graph algorithm implementation, and data transformations. Data Engineering : Solid experience building scalable data pipelines using modern tools such as Apache Spark, Kafka, Airflow, or dbt. Customer Data : Understanding of customer master data management, and entity resolution techniques. Cloud & DevOps : Experience with version control (Git) and familiarity with CI/CD processes and major cloud platforms (AWS, Azure).