Lead a dedicated team of Data Quality Engineers in upholding the accuracy, reliability, and integrity of extensive data platforms and pipelines. This pivotal role spearheads quality strategies, drives automation initiatives, and champions engineering excellence. You'll collaborate closely with Data Engineering, Product, and Analytics teams to ensure the delivery of dependable, business-critical data solutions.

Achieve success by cultivating high-performing teams through expert coaching and strong leadership. Drive superior data quality with scalable automation and robust testing strategies, enhancing data reliability across essential business platforms and pipelines. Establish high engineering standards that boost efficiency, consistency, and overall quality, ultimately delivering significant organizational impact through trusted data for informed business and product decisions.

Guide and develop a team of Data Quality Engineers across diverse projects and domains. Craft and implement comprehensive data quality, automation, and testing strategies for enterprise-scale data platforms. Oversee the validation of ETL pipelines, business rules, data transformations, and analytics workflows. Foster collaborative partnerships with Data Engineers, Product Managers, and Analysts to guarantee data accuracy and trustworthiness. Manage the design, development, and upkeep of scalable test automation frameworks.

Establish clear quality standards, best practices, and governance mechanisms for data engineering teams. Integrate CI/CD processes and automated quality checks seamlessly within data pipelines. Support the full employee lifecycle, including hiring, performance management, career advancement, and team development. Lead key technical initiatives and promote a culture of continuous improvement within quality engineering processes. Influence long-term quality strategy, tooling decisions, and overall organizational effectiveness.

Possess at least 8 years of experience in Data Quality Engineering, Software Testing, or Data Engineering, complemented by 2+ years in technical leadership or people management roles. Demonstrate strong proficiency in SQL and Python for validating large-scale datasets and data workflows. Experience with modern data platforms such as Snowflake, Databricks, Airflow, and Spark is essential.

Proven track record in building and scaling data quality automation frameworks. Solid understanding of ETL validation, data transformations, and business rule testing is required. Experience implementing CI/CD pipelines, quality controls, and industry-leading testing best practices is crucial. Exhibit a demonstrated ability to mentor, coach, and develop engineering talent. Excellent communication, stakeholder management, and cross-functional collaboration skills are expected. A Bachelor's degree in Computer Science, Engineering, or equivalent practical experience is required.