We are seeking a Manager, Data Quality Engineering (M2) to lead a dedicated team of Data Quality Engineers. This critical role focuses on ensuring the accuracy, reliability, and integrity of extensive data platforms and pipelines. You will spearhead quality strategies, drive automation initiatives, and foster engineering excellence. Collaboration with Data Engineering, Product, and Analytics teams is key to delivering trustworthy, business-essential data solutions.

Success in this position involves building high-performing teams through effective coaching and mentorship, driving data quality excellence with scalable automation, and improving data reliability across vital business platforms. You will establish robust engineering standards to enhance efficiency and consistency, ultimately delivering organizational impact by ensuring data integrity for crucial business and product decisions.

Lead and mentor a team of Data Quality Engineers, guiding them across diverse projects. Define and implement comprehensive data quality, automation, and testing strategies for enterprise-scale data platforms. Validate ETL pipelines, business rules, data transformations, and analytics workflows. Collaborate closely with Data Engineers, Product Managers, and Analysts to ensure data accuracy. Oversee the creation and maintenance of scalable test automation frameworks. Establish stringent quality standards, best practices, and governance for data engineering teams. Integrate CI/CD practices for automated quality checks within data pipelines. Support team members through hiring, performance management, and career development. Champion technical initiatives and drive continuous improvement in quality engineering processes. Influence long-term quality strategy, tooling decisions, and overall organizational effectiveness.

A minimum of 8 years of experience in Data Quality Engineering, Software Testing, or Data Engineering is required.

Candidates should possess at least 2 years of experience in a technical leadership or people management capacity.

Strong proficiency in SQL and Python is essential for validating large datasets and data workflows.

Hands-on experience with platforms such as Snowflake, Databricks, Airflow, and Spark is expected.

Demonstrated ability in building and scaling data quality automation frameworks.

A solid understanding of ETL validation, data transformations, and business rule testing is necessary.

Experience implementing CI/CD pipelines and embedding quality controls is crucial.

Proven capability in mentoring, coaching, and developing engineering talent.

Excellent communication, stakeholder management, and cross-functional collaboration skills are vital.

A Bachelor's degree in Computer Science, Engineering, or equivalent practical experience is required.