Graph Data Engineer

Summary

We are seeking a highly motivated and experienced Data Engineer with a strong focus on Graph Databases to join our growing data team. In this role, you will be responsible for designing, building, and maintaining robust and scalable graph-based data solutions. You will work closely with data scientists, analysts, and application developers to leverage the power of graph databases for complex data analysis, knowledge representation, and real-time applications.

Roles & Responsibilities

• Graph Data Modeling and Design:

1. Design and implement efficient and scalable graph data models using appropriate graph database technologies (e.g., Neo4j, Amazon Neptune, TigerGraph, etc.).
2. Translate business requirements into effective graph schemas and data structures.
3. Optimize graph models for performance and query efficiency.

• Graph Database Development and Management

  :

1. Develop and maintain graph database instances, including installation, configuration, and performance tuning.
2. Implement data ingestion and transformation pipelines to populate graph databases from various data sources.
3. Develop and optimize Cypher, Gremlin, or other graph query languages for complex data retrieval and analysis.
4. Implement data security and access control mechanisms for graph databases.

• Monitor and maintain the health of graph databases.

1. Design and implement ETL/ELT processes to integrate data from diverse sources into graph databases.
2. Develop and maintain data pipelines using tools like Apache Kafka, Apache Airflow, or similar technologies.
3. Ensure data quality and consistency throughout the data integration process.

• Graph Analytics and Application Development:

1. Collaborate with data scientists and analysts to develop graph-based analytics solutions.
2. Performance Optimization and Troubleshooting:

Desired Candidate Profile

2-5 years of experience in Data Engineering or a related field with expertise in Graph Databases (Neo4J).

Strong proficiency in Python programming language with hands-on experience working with PySpark.

Required Skills:

Graph Databases: Neo4j, Amazon Neptune, TigerGraph (or similar)

Query Languages: Cypher, Gremlin, SPARQL

Ontology & Semantic Modeling: RDF, OWL

Cloud Platforms: AWS, Azure, or GCP

Big Data Technologies: Hadoop, Spark, Kafka

Strong understanding of data modeling, ETL pipelines, and distributed systems