Shape the future of data reliability and performance as a Lead Data Quality Engineer (P4). You will champion quality strategies and automation across critical data, analytics, and commerce platforms. This role is pivotal in constructing robust data quality frameworks, ensuring the integrity of data pipelines, and spearheading initiatives that enhance trust, boost performance, and elevate the customer experience. Collaboration with Engineering, Product, and Data teams is key to establishing testing benchmarks, pioneering automation solutions, and embedding best engineering practices.

Drive the vision for data quality strategy, architecture, and automation. Design and implement advanced testing frameworks for both batch and streaming data pipelines. Uphold the accuracy, integrity, and reliability of extensive data platforms. Establish rigorous quality standards, monitoring protocols, and CI/CD practices for data workflows. Partner with cross-functional teams to deliver solutions that are both scalable and thoroughly testable. Lead comprehensive data quality programs from initial planning through to successful execution. Provide code reviews, mentor fellow engineers, and champion superior engineering practices. Evaluate and integrate tools that enhance automation, broaden coverage, and improve observability. Support Agile methodologies and offer technical leadership across diverse projects. Proactively identify opportunities to refine system performance, scalability, and overall quality.

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