This role leads a team of Data Quality Engineers, ensuring the accuracy, reliability, and integrity of large-scale data platforms. You will drive quality strategy, automation, and engineering excellence, collaborating closely with Data Engineering, Product, and Analytics teams to deliver trusted, business-critical data solutions.

Success involves building high-performing teams through coaching and leadership, driving data quality excellence with scalable automation and testing, and improving data reliability across critical business platforms. Establishing strong engineering standards and ensuring trusted data for business decisions are key outcomes.

Lead and mentor a Data Quality Engineering team, defining and executing comprehensive data quality, automation, and testing strategies for enterprise-scale data platforms.

Drive the validation of ETL pipelines, business rules, data transformations, and analytics workflows. Partner with Data Engineers, Product Managers, and Analysts to ensure data accuracy and trustworthiness. Oversee the design and maintenance of scalable test automation frameworks and establish quality standards and governance across data engineering teams.

Promote CI/CD integration and automated quality checks within data pipelines. Support team development through hiring, performance management, and career growth initiatives. Lead technical efforts and champion continuous improvement in quality engineering processes, influencing long-term quality strategy and tooling decisions.

A minimum of 8 years of experience in Data Quality Engineering, Software Testing, or Data Engineering is required, along with 2+ years in a technical leadership or people management capacity.

Proficiency in SQL and Python for validating large datasets and data workflows is essential. Experience with modern data platforms like Snowflake, Databricks, Airflow, and Spark is expected. You should have a proven track record of building and scaling data quality automation frameworks and a strong understanding of ETL validation, data transformations, and business rule testing.

Experience implementing CI/CD pipelines, quality controls, and testing best practices is necessary. Demonstrated ability to mentor, coach, and develop engineering talent is crucial, alongside strong communication, stakeholder management, and cross-functional collaboration skills. A Bachelor's degree in Computer Science, Engineering, or equivalent practical experience is required.