This role is for a Manager, Data Quality Engineering (M2) to lead a team focused on ensuring the accuracy and integrity of extensive data platforms. The position involves developing quality strategies, driving automation, and fostering engineering excellence in collaboration with Data Engineering, Product, and Analytics teams to deliver reliable data solutions.

Success in this role means building high-performing teams through effective coaching and leadership. It also involves driving data quality excellence with scalable automation and testing strategies, ultimately improving data reliability across critical business platforms and establishing robust engineering standards. The aim is to deliver significant organizational impact by ensuring trustworthy data for informed business and product decisions.

Leading and mentoring a team of Data Quality Engineers on various projects. Defining and implementing data quality, automation, and testing strategies for enterprise-scale data platforms. Validating ETL pipelines, business rules, data transformations, and analytics workflows. Collaborating with Data Engineers, Product Managers, and Analysts to guarantee data accuracy. Overseeing the development and maintenance of scalable test automation frameworks. Establishing quality standards, best practices, and governance across data engineering teams. Driving CI/CD integration and automated quality checks within data pipelines. Supporting team growth through hiring, performance management, and career development. Leading technical initiatives and promoting continuous improvement in quality engineering processes. Influencing long-term quality strategy, tooling decisions, and organizational effectiveness.

A minimum of 8 years of experience is required in Data Quality Engineering, Software Testing, or Data Engineering. Additionally, at least 2 years of experience in a technical leadership or people management capacity is necessary. Proficiency in SQL and Python is essential for validating large datasets and data workflows. Experience with modern data platforms like Snowflake, Databricks, Airflow, and Spark is expected. Proven ability to build and scale data quality automation frameworks is crucial. A strong understanding of ETL validation, data transformations, and business rule testing is required. Experience implementing CI/CD pipelines and quality controls, along with best testing practices, is beneficial. Demonstrated skills in mentoring, coaching, and developing engineering talent are important. Excellent communication, stakeholder management, and cross-functional collaboration abilities are necessary. A Bachelor's degree in Computer Science, Engineering, or equivalent practical experience is required.