
Data Engineer
4 weeks ago
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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:
- 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
Seniority level- Seniority levelEntry level
- Employment typeFull-time
- Job functionInformation Technology
- IndustriesIT Services and IT Consulting
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