Data Engineer

Mozn is a rapidly growing technology firm revolutionizing the field of Artificial Intelligence and Data Science headquartered in Riyadh, Saudi Arabia and it’s working to realize Vision 2030 with a proven track record of excellence in supporting and growing the tech ecosystem in Saudi Arabia and the GCC region. Mozn is the trusted AI technology partner for some of the largest government organizations, as well as many large corporations and startups. 

We are in an exciting stage of scaling the company to provide AI-powered products and solutions both locally and globally that ensure the growth and prosperity of our digital humanity. It is an exciting time to work in the field of AI to create a long-lasting impact.

We are looking for a skilled Data Engineer to join our team and contribute to the development of Mozn's Text-to-SQL product, Talk to Your Data. In this role, you will be responsible for designing, building, and optimizing the data pipelines that support our AI-driven SQL generation system. You will work closely with machine learning engineers, software developers, and product managers to ensure seamless data integration, efficient query execution, and high-performance system scalability. Additionally, you will collaborate with other teams to create high-quality queries and diverse question sets that improve model accuracy and system performance.

As a Data Engineer, your daily workload might include:

• Develop and maintain scalable data pipelines and ETL processes to support text-to-SQL model training and inference.
• Optimize SQL query generation by designing efficient database schemas, indexing strategies, and query execution plans.
• Collaborate with ML engineers to preprocess, transform, and structure large-scale datasets for AI model inference.
• Work with product, research, and domain experts to generate and curate realistic SQL queries and natural language questions that enhance the model’s ability to understand user intent.
• Monitor and enhance data pipeline performance, reliability, and scalability.
• Ensure data quality by implementing validation and logging mechanisms.
• Support real-time and batch query execution in the text-to-SQL system by optimizing database interactions.
• Stay up to date with best practices in data engineering, database optimization, and machine learning infrastructure.