This Lead Data Quality Engineer (P4) position spearheads the quality strategy and automation efforts for our data, analytics, and commerce platforms. You will be instrumental in building robust, scalable data quality frameworks, ensuring the reliability of our data pipelines, and leading initiatives to enhance data trust, performance, and customer satisfaction. This role involves close collaboration with Engineering, Product, and Data teams to define testing standards, implement automation solutions, and uphold engineering best practices.

Success in this role means developing scalable data quality platforms that bolster data reliability and trust. An automation-first approach will significantly reduce manual validation efforts. Establishing strong engineering standards across testing, monitoring, and CI/CD is key, alongside providing technical leadership that elevates team capabilities and delivery quality. Ultimately, you will drive significant business impact through improved data accuracy, performance, and customer experience.

Lead the overarching data quality strategy, architecture, and automation initiatives. Design and deploy advanced testing frameworks for both batch and streaming data pipelines. Uphold the accuracy, integrity, and reliability of large-scale data platforms. Establish comprehensive quality standards, monitoring mechanisms, and CI/CD practices for data workflows. Partner with cross-functional teams to deliver scalable, testable, and high-quality solutions. Manage data quality programs from inception through successful execution. Conduct code reviews, mentor junior engineers, and champion engineering best practices. Evaluate and integrate tools to enhance automation, coverage, and observability. Support Agile delivery processes and provide crucial technical leadership across multiple projects. Identify opportunities to optimize system performance, scalability, and overall quality.

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