Join our team as a Lead Data Quality Engineer and spearhead the quality strategy across our data, analytics, and commerce platforms. This pivotal role involves architecting robust, scalable data quality frameworks and ensuring the utmost reliability of our data pipelines. You will lead initiatives to enhance data trust, boost performance, and elevate the customer experience.

Collaboration is key, as you'll partner closely with Engineering, Product, and Data teams to define rigorous testing standards, implement advanced automation solutions, and champion engineering best practices.

Drive the vision, architecture, and automation efforts for data quality. Design and deploy sophisticated testing frameworks for both batch and streaming data pipelines. Ensure the accuracy, integrity, and unwavering reliability of vast datasets and platforms. Establish comprehensive quality standards, monitoring protocols, and CI/CD practices for data workflows. Partner with diverse teams to deliver solutions that are both scalable and thoroughly testable. Champion data quality programs from inception through successful completion. Provide code reviews, mentor fellow engineers, and cultivate a culture of engineering excellence. Evaluate and integrate tools to enhance automation, broaden coverage, and improve observability. Support Agile methodologies and offer technical guidance across multiple projects. Proactively identify opportunities to optimize system performance, scalability, and overall quality.

Demonstrate extensive experience in Data Quality Engineering, Data Engineering, or Software Testing. Possess advanced proficiency in SQL and Python, with a focus on validating large-scale datasets. Bring hands-on experience with data platforms like Snowflake, Databricks, Spark, or Airflow. Showcase a proven history of building and scaling automated data testing frameworks. Exhibit expertise in designing quality strategies for intricate data pipelines. Utilize strong debugging, analytical, and problem-solving capabilities. Have prior experience leading technical initiatives and mentoring engineering teams. Familiarity with AWS or other cloud-based data ecosystems is advantageous. Knowledge of data quality tools such as Great Expectations or Deequ is highly desirable. A Bachelor's degree in Computer Science or equivalent practical experience is required.