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Mid-level metro hiring but Neo4j niche reduces applicant density.
Graph and data engineering skills are transferable across industries but require Neo4j-specific expertise.
Explicit 4–6 years plus mandatory Neo4j, Cypher, Spark, and ETL skills enforce strict shortlisting.
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Design and develop enterprise Knowledge Graph solutions using Neo4j with a focus on data quality, performance, and scalability.
Build and maintain ETL pipelines ingesting data from multiple enterprise sources including RDBMS, APIs, and files into Neo4j.
Collaborate with cross-functional teams to deliver end-to-end graph data solutions and support production deployments and troubleshooting.
4-6 years of professional experience with hands-on Neo4j and Knowledge Graph development.
Strong skills in Cypher query writing and optimization, graph data modeling, and relationship design.
Experience building ETL pipelines using Cloudera ecosystem components (Hive, Spark/PySpark, HDFS, Impala) and integrating multi-source data into Neo4j.
Proficiency in SQL and Python programming; familiarity with Git and Agile development practices.
Experienced in designing and optimizing large-scale graph database solutions using Neo4j.
Comfortable managing end-to-end ETL workflows involving big data tools within Cloudera environments.
Able to collaborate effectively with architects, data engineers, and business teams to deliver scalable knowledge graph applications.