Data Engineer

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.

The candidate will have responsibilities across the following functions:

Graph Data Modelling and Design:

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

Graph Database Development and Management:

• Develop and maintain graph database instances, including installation, configuration, and performance tuning.
• Implement data ingestion and transformation pipelines to populate graph databases from various data sources.
• Develop and optimise Cypher, Gremlin, or other graph query languages for complex data retrieval and analysis.
• Implement data security and access control mechanisms for graph databases.
• Monitor and maintain the health of graph databases.

Data Integration and ETL/ELT:

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

Graph Analytics and Application Development:

• Collaborate with data scientists and analysts to develop graph-based analytics solutions.
• Develop and implement APIs and applications that leverage graph database capabilities.
• Develop and implement algorithms for graph traversals, community detection, and other graph analytics tasks.

Requirements:

Good to have:

• Graph Databases: Neo4j, Amazon Neptune, TigerGraph, Dgraph.
• Graph Query Languages: Cypher; ETL/ELT Development: Spark, Apache Airflow.
• Data Pipeline Tools: Apache Kafka, Apache Airflow.
• Programming: Python, PySpark, Unix.
• Databases: MongoDB, PostgreSQL, Redis.
• Semantic Web Technologies: RDF, OWL.
• REST APIs.
• Containerization and Orchestration: Docker, Kubernetes.
• Cloud Platforms: AWS, Azure, GCP.
• Machine Learning and Graph Analytics.