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Knowledge Graph Specialist

Roles & Responsibilities

  • 3-8 years of professional experience with data platforms, knowledge graphs, or graph databases
  • Strong hands-on experience with Neo4j including schema design, query optimization, and performance tuning
  • Proficiency in Cypher query language and graph data modeling techniques
  • Experience building and maintaining data pipelines and ETL workflows

Requirements:

  • Design, develop, and maintain knowledge graphs using Neo4j, ensuring efficient storage and retrieval of complex relational data.
  • Model real-world entities, relationships, and ontologies to represent domain knowledge in a graph-based structure.
  • Develop and optimize Cypher queries to support high-performance data retrieval and analytics.
  • Build scalable data pipelines to ingest, transform, and integrate data from multiple sources into graph databases.

Job description

This role is for one of the Weekday's clients

Min Experience

3 years

Location

Remote (India)

JobType

full-time

We are seeking a talented Knowledge Graph Specialist with strong expertise in Neo4j to design, build, and optimize scalable knowledge graph solutions that power advanced data relationships and intelligent applications. In this role, you will work at the intersection of data engineering, graph databases, and semantic modeling, helping organizations unlock deeper insights from complex and interconnected datasets.

The ideal candidate has hands-on experience working with graph databases, strong data modeling skills, and the ability to transform structured and unstructured data into meaningful graph representations. You will collaborate closely with data scientists, engineers, and product teams to build high-quality graph-based systems that support analytics, search, recommendation engines, and AI-driven applications.

Requirements

Key Responsibilities

  • Design, develop, and maintain knowledge graphs using Neo4j, ensuring efficient storage and retrieval of complex relational data.
  • Model real-world entities, relationships, and ontologies to represent domain knowledge in a graph-based structure.
  • Develop and optimize Cypher queries to support high-performance data retrieval and analytics.
  • Build scalable data pipelines to ingest, transform, and integrate data from multiple sources into graph databases.
  • Collaborate with cross-functional teams including data engineers, machine learning engineers, and product managers to implement graph-driven features and insights.
  • Ensure data quality, consistency, and integrity within the knowledge graph environment.
  • Optimize graph schema, indexing, and query performance for large-scale datasets.
  • Develop APIs and services that expose graph data for applications such as recommendation systems, semantic search, and AI models.
  • Document graph models, schemas, and technical processes to ensure maintainability and knowledge sharing.
  • Stay up to date with advancements in graph technologies, knowledge representation, and graph-based machine learning.

Required Qualifications

  • 3–8 years of professional experience working with data platforms, knowledge graphs, or graph databases.
  • Strong hands-on experience with Neo4j including schema design, query optimization, and performance tuning.
  • Proficiency in Cypher query language and graph data modeling techniques.
  • Experience building and maintaining data pipelines and ETL workflows.
  • Strong programming skills in languages such as Python, Java, or Scala.
  • Solid understanding of graph theory, semantic relationships, and ontology design.
  • Experience integrating graph databases with APIs, microservices, or data platforms.
  • Familiarity with data integration, data governance, and data quality practices.
  • Strong problem-solving skills with the ability to work on complex data challenges.

Preferred Qualifications

  • Experience working with large-scale knowledge graphs in production environments.
  • Familiarity with RDF, SPARQL, or semantic web technologies.
  • Experience with graph analytics, graph algorithms, or graph-based machine learning.
  • Exposure to cloud environments such as AWS, GCP, or Azure.
  • Understanding of use cases like entity resolution, recommendation engines, fraud detection, or semantic search.

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