Ne04J - Graph DB Engineering developer/Architect

TECHOAUTH SOLUTIONS LLC•Phoenix, AZ

9d•$72 - $75•Hybrid

About The Position

This role is for a Neo4j Graph Database Engineering developer/architect who will be a key contributor in designing and delivering graph data solutions. The position involves partnering with stakeholders to translate business needs into connected data models and graph architectures. The engineer will be responsible for engineering and maintaining Neo4j graph databases alongside relational and non-relational systems to support hybrid data environments. This includes developing and operationalizing relationship-based data models, designing and implementing knowledge graphs and connected data platforms, building and optimizing graph ingestion pipelines, and developing mechanisms and architectures to support business line-specific use cases. The role also involves establishing standards and best practices for graph modeling, schema evolution, and governance, and reviewing and managing interfaces supporting graph data access. Collaboration with data science and analytics teams to enable graph-based feature engineering and machine learning integration is also a key aspect of this position. The company aims to advance the organization’s data architecture toward connected, relationship-driven models, establish graph-first design patterns, integrate Neo4j into the broader enterprise data ecosystem, promote adoption of knowledge graphs and semantic modeling, implement scalable, resilient graph data platforms, and standardize graph engineering practices. The role will partner with architecture leadership to define the future-state connected data vision, aligned with digital and AI strategies.

Requirements

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.
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.

Nice To Haves

10+ years of experience in data engineering, including leading engineers and technical teams.
Experience with visualization and BI tools for graph-derived insights (e.g., KeyLines, Bloom).

Responsibilities

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.
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.