SC Graph Data Engineer

Harvey Nash
United States
6 days ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours

Tech stack

Airflow Amazon Web Services Information Engineering Data Integration Extract Transform Load (ETL) Data Structures Data Systems Distributed Computing Environment Graph Database Python (Programming Language) Neo4j Workflow Management Systems
+2 more
Apache Spark Data Pipelines

Job description

We are seeking an experienced Graph Data Engineer to join our Data Engineering team on a freelance basis. You will work closely with data engineers, software engineers, architects and business stakeholders to design, develop and maintain scalable, high-performance data solutions, with a particular focus on graph databases, data modelling, entities and relationships.

Requirements

This is a hands-on engineering role suited to someone with strong commercial experience in Neo4j, Cypher, AWS and Python, alongside a proven ability to build robust, scalable data pipelines and deliver effective data integration and transformation solutions., Graph Data Engineering

  • Strong commercial experience working with Neo4j.
  • Advanced proficiency in Cypher, including query development and optimisation.
  • Strong understanding of graph data modelling, including entities, relationships and connected data structures.
  • Experience designing and implementing ETL/ELT pipelines for graph databases.
  • Ability to develop scalable and efficient data solutions to support complex data relationships.

AWS and Data Engineering

  • Strong experience working with AWS in data engineering environments.
  • Hands-on experience with AWS Glue for data integration and processing.
  • Strong experience using Apache Airflow for workflow orchestration and pipeline management.
  • Advanced Python development skills.
  • Experience with Apache Spark or similar distributed data processing technologies would be advantageous.
  • Proven experience designing, building and maintaining scalable ETL/ELT pipelines.
  • Strong understanding of data integration, transformation and processing best practices.

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