The role involves designing and implementing a data quality framework across various data layers. The engineer will build and maintain automated quality checks within data pipelines to ensure data reliability and consistency. Responsibilities include performing reconciliation between source systems and data platforms to verify accurate transformations. The position requires validating identity resolution outputs and conducting end-to-end pipeline testing. Additionally, the engineer will partner with technical and business teams to define acceptance criteria and support user acceptance testing. Finally, the role entails documenting quality assurance processes and monitoring pipeline health to resolve data incidents.

The role requires at least three years of experience in data engineering or data quality positions. Candidates must possess strong SQL skills and experience working with Azure-based data platforms, specifically Databricks. Proficiency in validating ETL/ELT processes and layered architectures is necessary, along with the ability to implement automated validation and reconciliation. Advanced English communication skills are required to interact with global teams.