Ne04J - Graph DB Engineering developer/Architect - Full-time

Job Title: Ne04J - Graph DB Engineering developer/Architect

Serve as a key contributor in designing and delivering graph data solutions, partnering with stakeholders to translate business needs into connected data models and graph architectures.

Engineer and maintain Neo4j graph databases alongside relational and non-relational systems to support hybrid data environments

Develop and operationalize relationship-based data models, including nodes, edges, and properties aligned to enterprise business domains

Design and implement knowledge graphs and connected data platforms that unify disparate data sources and expose relationships across systems.
Build and optimize graph ingestion pipelines for batch and streaming data sources, ensuring data freshness and integrity

Develop mechanisms and architectures to support business line–specific use cases. 
Establish standards and best practices for graph modeling, schema evolution, and governance within the enterprise data ecosystem.
Review and manage interfaces supporting graph data access, including APIs, visualization tools, and analytics platforms.

Partner with data science and analytics teams to enable graph-based feature engineering and machine learning integration.

Deep expertise in Neo4j platform capabilities, including clustering, security, and enterprise deployment patterns

Experience in graph data modeling and ontology design for complex enterprise datasets

Knowledge of connected data architecture patterns, including knowledge graphs and data fabrics

Experience integrating graph platforms with big data ecosystems (Spark, Kafka, etc.) and cloud-native services

Strong understanding of query optimization, indexing, and graph performance tuning

Experience with data ingestion frameworks supporting both batch and real-time pipelines

Proficiency in Python

10+ years of experience in data engineering, including leading engineers and technical teams

Proven experience implementing Neo4j in enterprise environments

Familiarity with machine learning and AI techniques leveraging graph data

Experience working in Agile environments and leading cross-functional delivery teams

Experience with visualization and BI tools for graph-derived insights (e.g., KeyLines, Bloom)

Advance the organization’s data architecture toward connected, relationship-driven models, complementing existing platforms

Establish graph-first design patterns where relationship complexity drives business value

Integrate Neo4j into the broader enterprise data ecosystem (cloud, lakehouse, streaming platforms)

Promote adoption of knowledge graphs and semantic modeling to improve interoperability and reuse

Implement scalable, resilient graph data platforms aligned with enterprise security and compliance standards

Standardize graph engineering practices, including modeling guidelines, performance tuning, and operational monitoring

Partner with architecture leadership to define the future-state connected data vision, aligned with digital and AI strategies