Data Quality Assurance Engineer (Automation & Observability)

Fetcherr experts in deep learning, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions.

Fetcherr experts in deep learning, algo-trading, e-commerce, and digitization, Fetcherr disrupts traditional systems with its cutting-edge AI technology. At its core is the Large Market Model (LMM), an adaptable AI engine that forecasts demand and market trends with precision, empowering real-time decision-making. Specializing initially in the airline industry, Fetcherr aims to revolutionize industries with dynamic AI-driven solutions. 

We’re looking for a Data Quality Assurance Engineer to join our Price Optimization Department with a focus on automation, observability, and performance. In this role, you will design and implement validation frameworks in code, build automation around data quality monitoring, and contribute to the observability and performance testing of large-scale pipelines. You will collaborate closely with data engineers, scientists, and analysts to ensure our AI-driven models operate on reliable, high-quality data. The ideal candidate will be a strong Python developer with hands-on experience in data frameworks and distributed systems, combined with solid knowledge of OLAP databases and data pipeline concepts. 

Key responsibilities 

• Design and implement automated validation procedures for pipelines and transformations
• Develop monitoring and observability tools to ensure data quality and reliability
• Contribute to performance testing and optimization of large-scale data systems
• Build and maintain reusable Python-based validation frameworks  
• Collaborate with data engineers, scientists, and analysts to define quality metrics and integrate them into observability dashboards 
• Investigate complex issues in distributed data systems and contribute fixes and improvements Technical requirements