Ne04J Graph DB Engineering developerArchitect

TECHOAUTH SOLUTIONS LLC; Phoenix (AZ); Hybrid; USD 130,000 - 160,000; Full time

Benefits offered by this job

Flexible work from home options

Job summary

TECHOAUTH SOLUTIONS LLC is seeking a Graph DB Engineering Developer/Architect to design and maintain graph data solutions using Neo4j in Phoenix, AZ. The ideal candidate has over 10 years of experience in data engineering and will lead teams, integrate graph solutions, and implement knowledge graphs.

This hybrid role offers flexible work-from-home options. Candidates should possess deep technical expertise and experience in big data platforms, machine learning, and Agile environments.

Qualifications

• 10+ years of experience in data engineering, including leading engineers.
• Proven experience implementing Neo4j in enterprise environments.
• Familiarity with machine learning and AI techniques leveraging graph data.

Responsibilities

• Design and deliver graph data solutions for stakeholders.
• Engineer and maintain Neo4j databases in hybrid environments.
• Develop relationship-based data models for business domains.
• Implement knowledge graphs and optimize data ingestion pipelines.

Skills

Neo4j platform capabilities

Graph data modeling

Connected data architecture

Python

Big data integration

Query optimization

Data ingestion frameworks

Agile environments

Visualization and BI tools

Education

10+ years in data engineering

Tools

Neo4j

Spark

Kafka

Job description

Job Title: Ne04J - Graph DB Engineering developer/Architect

Location: Phoenix, AZ (Hybrid)

Employment Type: W2

Specialized 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.

Preferred Technical Expertise

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.

Preferred Qualifications

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).

Modernization and Architecture Expectations

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.

Flexible work from home options available.