Seeking an experienced Graph Data Engineer/Knowledge Graph Specialist to architect, construct, and refine robust graph-based data solutions. This role requires deep expertise in graph databases, ontologies, and graph analytics, complemented by practical experience with Python, Azure, Databricks, Spark, and SQL. You will be instrumental in developing enterprise-grade knowledge graphs, facilitating advanced data relationships, semantic modeling, and deriving actionable insights through graph analytics.

This position offers the chance to work with cutting-edge graph and semantic technologies on large-scale data platforms utilizing Azure and Databricks, within a collaborative and innovative environment.

Key responsibilities include designing, developing, and maintaining graph databases (e.g., Neo4j, Stardog) and building optimized knowledge graphs for extensive structured and unstructured datasets. You will model complex relationships using graph schemas and ontology frameworks, and develop/implement ontologies, taxonomies, and semantic data models. This involves working with RDF, OWL, and SPARQL for semantic data representation and querying, ensuring data consistency and integrity.

Constructing scalable data pipelines with Python, PySpark, Spark, and SQL is essential for ingesting, transforming, and integrating data. You will leverage Azure Data Services (Data Lake, Synapse, Databricks) for these tasks. Performing graph analytics, such as centrality and pathfinding, and developing algorithms for use cases like fraud detection and recommendation systems will be crucial. Deployment and management of graph solutions on Microsoft Azure, utilizing Databricks for large-scale processing, and optimizing performance are also key areas.

Collaboration with data scientists, engineers, and business teams to define requirements and translate complex business challenges into graph-based solutions is expected. You will also provide technical guidance and champion best practices.

This role demands a minimum of 5 years of experience in data engineering or graph-based solutions, with strong proficiency in Python, SQL, and Spark (PySpark). Essential is hands-on experience with at least one graph database such as Neo4j, Stardog, AllegroGraph, or TigerGraph, alongside practical application of Microsoft Azure services including Data Lake, Databricks, and Synapse. A solid understanding of graph data modeling and graph theory fundamentals is required, along with familiarity with graph query languages like Cypher, SPARQL, or Gremlin.

Beneficial skills include knowledge of RDF, OWL, and Linked Data standards, experience with entity resolution or master data management, and familiarity with data governance and metadata management tools. Exposure to ML/AI use cases on graph data is also advantageous. A Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field is necessary.

Preferred experience includes building enterprise-scale knowledge graphs, working with semantic web technologies, and implementing graph solutions within domains like finance, retail, healthcare, or telecom.