Graph Database Developer

Job Title: Graph Database developer

Location: Mumbai, MH
Skills required: NoSQL Graph DB
Experience range: 8 to 10 years

Role Descriptions:

• We are seeking a highly skilled Graph Database developer to support the design and development of advanced graph-based data solutions in a regulated environment (e.g., pharma clinical development and RD).
• This role involves hands-on engineering to build scalable graph architectures, optimize graph queries, and ensure production ready implementation of graph data models and graph-powered applications.

Key Responsibilities:

• Design, develop, and maintain graph data models and schemas using platforms such as Neo4j, Amazon Neptune, or Tiger Graph.
• Build and optimize graph query logic using Cypher, Gremlin, SPARQL, or GSQL.
• Develop ingestion and transformation pipelines to load structured and unstructured data into graph databases.
• Integrate graph databases with application services, APIs, and analytics pipelines.
• Collaborate closely with data science, data engineering, and platform teams to support AI driven relationship analytics (e.g., knowledge graphs, entity resolution, graph ML).
• Contribute to system architecture and design to ensure scalability, high availability, and performance of graph-based systems.
• Engage confidently with business stakeholders and cross functional teams on graph related use cases, capabilities, and solution designs.

Qualifications:

• Proven experience with major graph database technologies (Neo4j, Cosmos DB Gremlin API, Neptune, Tiger Graph, or equivalent).
• Strong proficiency in graph query languages such as Cypher, Gremlin, SPARQL, or GSQL.
• Understanding of graph theory, graph modeling, and relationship-centric data design. Experience with data engineering concepts, pipelines, and ingestion frameworks.
• Excellent collaboration and communication skills.
• Ability to work in fast paced, highly regulated environments.
• Prior experience in pharma or other regulated industries is a plus.
• Experience building or supporting knowledge graphs is a significant advantage.
• Familiarity with integrating graph databases into AIML systems is a plus.
• Understanding of performance tuning, indexing strategies, and query optimization for graph workloads is a strong plus.