Graph database expertise: A deep understanding of graph database concepts and proven experience with Neo4j is crucial.
Query languages: Strong proficiency in the Neo4j query language, Cypher, is required.
Programming languages: Skills in Python or Java are often needed for scripting data ingestion, automation, and integrating with other systems.
Analytical skills: Excellent problem-solving, critical thinking, and analytical abilities for interpreting complex datasets.
Data warehousing: Experience with cloud data platforms and other databases, including traditional SQL, is often necessary for data integration.
Data visualization: Experience with data visualization tools like Tableau or Power BI is valuable for presenting findings clearly.
Domain knowledge: An understanding of the business domain, such as finance or healthcare, is important for contextualizing data and analysis
Roles & Responsibilities
Graph data modeling: Design and refine the structure of data as nodes and relationships within a graph database to best serve specific analytical needs.
Data ingestion and processing: Build data pipelines to extract, transform, and load (ETL) data from various sources into the Neo4j graph database.
Cypher query development: Write and optimize complex Cypher queries to explore data patterns, find connections, and perform deep-link analysis.
Graph algorithm implementation: Apply graph algorithms (e.g., centrality, community detection, pathfinding) to answer complex business questions related to network structures.
Data analysis and visualization: Perform exploratory analysis and create dashboards, reports, and data visualizations to communicate findings to both technical and non-technical stakeholders.
Collaboration: Work with other teams, including data scientists, data engineers, and business leaders, to understand data requirements and deliver actionable insights.
Maintenance and performance tuning: Monitor the performance of Neo4j queries and the overall database and make adjustments for efficiency and scalability.