Backend Developer with Graph Database Knowledge::San Diego, CA (Onsite

Job Description

Job Title: Backend Java Developer with Graph Database Knowledge

Location: San Diego, CA (Onsite)

Pay Rate : ON W2/C2C/Full time

Skills: Backend Java, Graph Knowledge like (Neo4j or Cypher or Gremlin)

Job Summary:

We are looking for a talented Backend Java Developer with hands-on experience in graph databases to join our growing engineering team. You will be responsible for developing scalable and efficient backend services using Java, and designing and integrating complex data models with graph database systems such as Neo4j or Amazon Neptune.

Key Responsibilities:

Design, develop, and maintain backend services and APIs using Java (Spring Boot or similar frameworks)

Build and optimize graph-based data models and queries using technologies like Neo4j, Cypher, or Gremlin

Integrate graph databases with microservices to deliver advanced querying capabilities and relationship-based analytics

Collaborate with front-end developers, data engineers, and product managers to deliver end-to-end solutions

Ensure code quality through testing, code reviews, and adherence to software development best practices

Monitor and troubleshoot system performance, especially around data modeling and query execution in graph systems

Design and implement secure and efficient data access and storage strategies

Required Qualifications:

Bachelor's or Master's degree in Computer Science, Engineering, or related field

3+ years of backend development experience in Java (Spring Boot, Hibernate, etc.)

Hands-on experience with graph databases such as Neo4j, Amazon Neptune, ArangoDB, or OrientDB

Strong understanding of graph theory and data modeling techniques for graphs

Proficiency in graph query languages such as Cypher or Gremlin

Familiarity with RESTful APIs, JSON, and HTTP protocols

Experience with relational and NoSQL databases

Knowledge of version control tools (e.g., Git) and CI/CD workflows

Preferred Qualifications:

Experience designing recommendation systems, fraud detection, or knowledge graphs

Understanding of GraphQL and its integration with graph databases

Familiarity with containerization (Docker) and orchestration tools (Kubernetes)

Exposure to cloud platforms like AWS, GCP, or Azure (especially services like Amazon Neptune)

Performance tuning of large-scale graph databases

Permanent