Data Quality Engineer, Data Reliability Platform

Client Our client is a leading global professional education organization focused on advancing investment knowledge and credentialing standards. Team You will join a cross functional team of data engineers, analytics specialists, and quality experts. The team collaborates closely, communicates frequently, and works in an environment where data correctness and reliability are key priorities. Work is structured and iterative, with regular stakeholder interactions. Position overview You will work on a platform focused on improving data accuracy, trust, and reliability across analytical and operational environments. The project includes implementing monitoring rules, building automated validation workflows, and supporting data teams with insights that help maintain data quality at scale. The platform is actively evolving and provides space for ownership and continuous improvement. Technology stack Monte CarloSodaGreat ExpectationsSQLPythonCloud data platformsData pipeline architecturesMetadata and data governance tooling Responsibilities • Design and implement data quality monitoring rules using Monte Carlo, Soda, Great Expectations or equivalent tools • Perform data profiling to assess completeness, consistency, and accuracy • Collaborate with data engineering and analytics teams to define data quality needs • Develop automated checks and alerts as part of data pipelines • Support remediation workflows and participate in root cause analysis • Document rules, processes, and metrics supporting data quality governance • Contribute to continuous improvement initiatives and propose enhancements • Communicate findings and recommendations to technical and non technical stakeholders Requirements • Experience working with data quality tools such as Monte Carlo, Soda, or Great Expectations • Hands on background in data profiling, validation, and monitoring • Experience using SQL for analysis and data validation tasks • Familiarity with Python or similar scripting languages for automation • Understanding of data pipeline principles and cloud data platforms • Knowledge of data governance concepts and quality frameworks • Clear communication skills and the ability to collaborate in a distributed team