
Graph Database Expert
6 days ago
Job Description
We are seeking an experienced Graph Database Expert to join our team.
- This role requires a deep understanding of data engineering, graph querying languages, and data modeling. The ideal candidate will have hands-on experience with graph databases, including design, implementation, and maintenance of ETL pipelines.
- The successful candidate will develop and optimize graph database solutions using Cypher, SPARQL, or GQL querying languages. Experience with Neo4J DB is a plus.
- Key responsibilities include building and maintaining ontologies and knowledge graphs, ensuring efficient and scalable data modeling, as well as integrating vector databases and implementing similarity search techniques.
- Collaboration with data scientists and engineers to operationalize machine learning models and integrate with graph databases is also essential.
- Additionally, the Graph Database Expert will work with Large Language Models (LLMs) to achieve functional and business objectives.
- Ensuring data quality, integrity, and security while delivering robust and scalable solutions is crucial.
- Effective communication with stakeholders to understand business requirements and deliver solutions that meet objectives is vital.
Requirements:
- At least 5 years of hands-on experience in data engineering, with at least 2 years working with graph databases.
Skills:
- Querying: Advanced knowledge of Cypher, SPARQL, or GQL querying languages.
- ETL Processes: Expertise in designing and optimizing ETL processes for graph structures.
- Data Modeling: Strong skills in creating ontologies and knowledge graphs.
- Vector Databases: Understanding of similarity search techniques.
- LLMs: Experience working with Large Language Models for functional objectives.
- Communication: Excellent verbal and written communication skills.
Desirable Skills:
- Cloud Platforms: Experience with Azure analytics platforms, including Function Apps, Logic Apps, and Azure Data Lake Storage (ADLS).
- Graph Analytics: Familiarity with graph algorithms and analytics.
- Agile Methodology: Hands-on experience working in Agile teams and processes.
- Maching Learning: Understanding of machine learning models and their implementation.
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