Data Engineer - Neo4j

DATAECONOMY•Boston, MA

33d

About The Position

Experienced Neo4j professional with deep expertise in Cypher development and hands‑on experience designing, building, and deploying knowledge graphs. In this role, you’ll architect graph data models, implement high‑performance queries, and collaborate closely with data engineers, domain experts, and product teams to bring graph‑based intelligence to life.

Requirements

6+ years of hands‑on experience with Neo4j in production environments.
Expert‑level proficiency in Cypher, including query optimization, pattern matching, and advanced graph operations.
Proven experience designing and implementing knowledge graphs, ontologies, or semantic data models.
Strong understanding of graph theory, graph algorithms, and data modeling principles.
Experience with APOC procedures, GDS library, and Neo4j tooling.
Familiarity with data integration frameworks (e.g., Kafka, Spark, Python ETL, GraphQL, REST APIs).
Solid understanding of database performance tuning, indexing, and query profiling.
Proficiency in at least one programming language commonly used with Neo4j (e.g., Python, Java, JavaScript).

Responsibilities

Design and implement knowledge graphs that accurately represent complex business domains, including ontology development, schema design, and entity‑relationship modeling.
Develop, optimize, and maintain Cypher queries, procedures, and graph algorithms for both transactional and analytical workloads.
Architect Neo4j solutions, including data ingestion pipelines, indexing strategies, and performance tuning.
Collaborate with cross‑functional teams to translate business requirements into graph‑based solutions and integrate Neo4j with existing systems.
Implement ETL/ELT processes to load structured and unstructured data into Neo4j using tools such as APOC, Graph Data Science (GDS), or custom pipelines.
Ensure data quality, consistency, and governance across graph models and related systems.
Monitor and maintain Neo4j environments, including clustering, backups, upgrades, and security configurations.
Document graph models, design decisions, and best practices to support internal knowledge sharing.