Join our team as a Manager, Data Quality Engineering (M2) and lead a dedicated group of Data Quality Engineers. You will be instrumental in ensuring the accuracy, reliability, and overall integrity of our extensive data platforms and pipelines. This pivotal role involves shaping our quality strategy, driving automation initiatives, and fostering engineering excellence. You'll collaborate closely with Data Engineering, Product, and Analytics teams to deliver dependable, business-critical data solutions.

Success in this role means building and nurturing high-performing teams through effective coaching and leadership, driving unparalleled data quality with scalable automation, and significantly improving data reliability across vital business platforms. You will also be responsible for establishing robust engineering standards that enhance efficiency and consistency, ultimately delivering organizational impact through trusted data for crucial business and product decisions.

Lead and develop a team of Data Quality Engineers across diverse projects and domains. Define and implement comprehensive data quality, automation, and testing strategies for enterprise-level data platforms. Drive the validation of ETL pipelines, intricate business rules, data transformations, and analytical workflows. Collaborate with Data Engineers, Product Managers, and Analysts to guarantee data accuracy and trustworthiness. Oversee the creation and upkeep of scalable test automation frameworks.

Establish stringent quality standards, best practices, and governance frameworks for data engineering teams. Integrate CI/CD practices and automated quality checks directly into data pipelines. Support team growth through effective hiring, performance management, and career development. Spearhead technical initiatives and champion 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 essential. Prior experience of at least 2 years in a technical leadership or people management capacity is required. Demonstrable expertise in SQL and Python for validating large-scale datasets and data workflows is crucial. Familiarity with modern data platforms such as Snowflake, Databricks, Airflow, and Spark is expected.

Proven ability to build and scale data quality automation frameworks. A strong grasp of ETL validation, data transformations, and business rule testing is necessary. Experience implementing CI/CD pipelines, quality controls, and testing best practices is a key requirement. You must possess a demonstrated ability to mentor, coach, and develop engineering talent. Excellent communication, stakeholder management, and cross-functional collaboration skills are vital for this role. A Bachelor's degree in Computer Science, Engineering, or equivalent practical experience is required.