Senior Data Engineer (Graph DB specialist)

About Mindcurv (Part of Accenture Song)

We help our customers rethink their digital business, experiences, and technology to navigate the new digital reality. We do this by designing sustainable and accountable solutions for humans living in a digital world. Mindcurv holistically covers the markets need to digitalise business processes and customer experiences and take advantage of the cloud following DevOps and agile principles.

Your role

We are seeking a highly skilled 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.

Who you are

• Experience: At least 5 years of hands-on experience in data engineering. With 2 years of experience working with Graph DB.
• 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.

Nice-to-Have Qualifications

• 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.
• Machine Learning: Understanding of machine learning models and their implementation.