Data Engineer (Generative AI)

Full Description

Job Title: Data Engineer – Knowledge Graph & AI Integration

Job Description:

We are seeking a highly capable Data Engineer to drive our data infrastructure as we build AI-powered systems such as chatbots and automated report generators. This role is centered on solving data-centric challenges, from transforming and migrating data to ensuring consistency, integrity, and scalability across systems.

The ideal candidate will have a deep understanding of data engineering principles, a strong command over Python, and experience working with MongoDB and knowledge graph technologies. You’ll play a critical role in enabling our Graph-based Retrieval-Augmented Generation (Graph RAG) system by building data pipelines that feed reliable, structured, and query-able knowledge into AI models.

Key Responsibilities:

• Design and implement robust data pipelines to extract, clean, and transform data from MongoDB into graph-based formats.

• Migrate and maintain data in knowledge graph databases (e.g., Neo4j, TigerGraph), ensuring alignment with evolving schema and AI use cases.

• Ensure data correctness and consistency across all stages of the pipeline by implementing rigorous validation, monitoring, and fallback mechanisms.

• Handle failure gracefully in pipelines with proper alerting, retries, and state recovery strategies to maintain reliability and uptime.

• Collaborate with AI teams to structure data for Graph RAG-based context retrieval, enhancing the effectiveness of LLM-driven modules.

• Develop utilities and tools in Python to support data manipulation, export/import routines, and graph schema enforcement.

• Contribute to ontology and data model design to reflect domain knowledge in structured, queryable forms.

Required Qualifications:

• Minimum Experience of 3 years in Data Engineering and Generative AI.

• Strong proficiency in Python, especially for data processing and automation.

• Solid foundation in data structures, algorithms, and software design principles.

• Experience with MongoDB and handling large-scale semi-structured data.

• Familiarity with knowledge graph databases (e.g., Neo4j, TigerGraph) and graph data models.

• Understanding of ETL processes, data validation, and pipeline orchestration.

• Proven ability to assure data correctness and manage failure cases in real-world data systems.

• Basic knowledge of AI concepts, especially LLMs and Retrieval-Augmented Generation.

Nice to Have:

• Experience working directly with Graph RAG pipelines or other knowledge-enhanced AI systems.

• Familiarity with graph query languages like Cypher or SPARQL.

• Exposure to cloud-based data platforms, workflow orchestrators (e.g., Airflow), or containerized environments (e.g., Docker).

• Understanding of semantic data modeling, ontologies, or linked data concepts.

Preferred Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or a related field.

• Experience working with large datasets and modern data tools (like Airflow, dbt, Spark, etc.).

• Previous experience supporting data science, analytics, or machine learning teams.

Office Location: DHA Phase 2, Street 8