Graph Data Engineer - Active TS/SCI

Seeking a Data Engineer with expertise in graph databases to join our dynamic team. The ideal candidate will have a strong background in data engineering, graph querying languages, and data modeling, with a keen interest in leveraging cutting-edge technologies like vector databases and LLMs to drive functional objectives.

Your responsibilities will include:

• Design, implement, and maintain ETL pipelines to prepare data for graph-based structures.
• Develop and optimize graph database solutions using querying languages such as Cypher, SPARQL, or GQL. Neo4J DB experience is preferred. 
• Build and maintain ontologies and knowledge graphs, ensuring efficient and scalable data modeling.
• Integrate vector databases and implement similarity search techniques, with a focus on Retrieval-Augmented Generation (RAG) methodologies and GraphRAG.
• Collaborate with data scientists and engineers to operationalize machine learning models and integrate with graph databases.
• Work with Large Language Models (LLMs) to achieve functional and business objectives.
• Ensure data quality, integrity, and security while delivering robust and scalable solutions.
• Communicate effectively with stakeholders to understand business requirements and deliver solutions that meet objectives.

Qualifications:

• Experience: At least 5 years of hands-on experience in data engineering. With 3 years of experience working with Graph DB. Engineering experience within AWS systems is highly preferred
• Programming: Proficiency in Python and PySpark programming.
• 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. Presenting data for Graph RAG based solutions
• Vector Databases: Understanding of similarity search techniques and RAG implementations.
• LLMs: Experience working with Large Language Models for functional objectives.
• Communication: Excellent verbal and written communication skills.