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Graph Jobs (NOW HIRING)

Trust IT LLC

Lead Knowledge Graph Engineer

Collegeville, PA · On-site

$101K - $133K/yr

We are seeking a Lead Knowledge Graph Engineer for a high-priority, 12+ month contract on-site in Upper Providence, PA (12 days/month hybrid schedule). In this role, you will act as the technical ...

Infosys Limited Digital

Graph DB Developer

Plano, TX · On-site

... Graph Databases Technical Skills 2 Technology|Big Data - Data Processing|Spark Technical Skills 3 Technology|data science|PYTHON Overview The Infosys Financial Services unit is a global leader in ...

Adobe

Principal Product Manager, Graph

Seattle, WA

Graph is a highly technical product used to build, visualize, implement, and share complex workflows through graph-based systems. Firefly Graph powers both an advanced node-based editor and ...

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How much do graph jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for graph in the United States is $31.03, according to ZipRecruiter salary data. Most workers in this role earn between $15.87 and $25.96 per hour, depending on experience, location, and employer.

How does a Graph Database Engineer typically collaborate with data scientists and software developers in a project setting?

Graph Database Engineers often work closely with data scientists to design and optimize data models that support complex relationships and queries. They collaborate with software developers to integrate graph databases into applications, ensuring seamless data flow and performance. Regular meetings and code reviews help align database structures with business requirements and analytical goals. This cross-functional teamwork is essential for delivering scalable, high-performing solutions that leverage graph-based data.

What is a Graph job?

A Graph job typically refers to a position involving the analysis, visualization, or implementation of graph-based data structures and algorithms. This may include working with graph databases, network analysis, or machine learning applications that leverage graph theory. Common roles include Graph Data Scientist, Graph Engineer, or Network Analyst, often requiring expertise in tools like Neo4j, GraphQL, or NetworkX. These jobs are commonly found in industries such as social networks, cybersecurity, recommendation systems, and logistics.

What is the difference between Graph vs Data Analyst?

AspectGraphData Analyst
Required CredentialsTypically no formal degree, but knowledge of graph theory helpsBachelor's or higher in data science, statistics, or related fields
Work EnvironmentResearch, academia, or specialized tech rolesBusiness, finance, healthcare, and various industries
Employer & Industry UsageUsed in computer science, mathematics, and research projectsApplied in analyzing data trends, reporting, and decision-making

While a graph refers to a mathematical or visual representation of data, a Data Analyst is a professional who interprets data, often using graphs as tools. The Data Analyst's role involves analyzing data sets, creating visualizations, and providing insights, whereas a graph is a component or tool used within data analysis processes.

What are Graph jobs?

Graph jobs typically refer to roles that involve working with graph data structures, graph databases, or graph theory. These jobs can include positions such as data scientists, software engineers, or researchers who analyze relationships between data points, model complex networks, or optimize algorithms for graph traversal. Graph jobs are commonly found in industries like technology, finance, telecommunications, and social media, where understanding connections and networks is crucial. Professionals in these roles often use tools such as Neo4j, GraphQL, or NetworkX to handle and analyze graph data. A strong background in mathematics, computer science, or data analysis is often required.

What are the key skills and qualifications needed to thrive as a Graphic Designer, and why are they important?

To thrive as a Graphic Designer, you need a strong foundation in design principles, creativity, and proficiency in visual communication, often supported by a degree in graphic design or a related field. Mastery of technical tools such as Adobe Creative Suite (Photoshop, Illustrator, InDesign) and knowledge of digital asset management systems are typically required. Excellent communication, time management, and collaboration skills help designers effectively convey ideas and work with clients or teams. These skills are essential to producing compelling visuals that meet client goals and stand out in a competitive creative industry.
More about Graph jobs
What cities are hiring for Graph jobs? Cities with the most Graph job openings:
What states have the most Graph jobs? States with the most job openings for Graph jobs include:
What job categories do people searching Graph jobs look for? The top searched job categories for Graph jobs are:
Graph jobs near you
Infographic showing various Graph job openings in the United States as of July 2026, with employment types broken down into 68% Full Time, 30% Part Time, and 2% Contract. Highlights an 60% Physical, 2% Hybrid, and 38% Remote job distribution, with an average salary of $64,550 per year, or $31 per hour.

Ne04J - Graph DB Engineering developer/Architect

TECHOAUTH SOLUTIONS LLC

Phoenix, AZ • On-site

$72 - $75/hr

Full-time

Posted 17 days ago

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.