This pivotal role focuses on spearheading data quality strategy and automation within our data, analytics, and commerce platforms. As a Lead Data Quality Engineer, you will architect and implement robust data quality frameworks, ensuring the unwavering reliability of data pipelines. Your efforts will drive quality initiatives that enhance data trust, optimize performance, and elevate customer experiences. You will collaborate closely with Engineering, Product, and Data teams to solidify testing standards, pioneer automation solutions, and champion engineering excellence.

Success in this position is measured by the creation of scalable data quality platforms that bolster data reliability and trust. An automation-first mindset is crucial for minimizing manual validation efforts. You will uphold strong engineering standards in testing, monitoring, and CI/CD, providing technical leadership that amplifies team capabilities and delivery quality. Ultimately, your contributions will yield significant business impact through enhanced data accuracy, improved performance, and a superior customer journey.

Spearhead the development and implementation of comprehensive data quality strategy, architecture, and automation initiatives. Design and deploy advanced testing frameworks tailored for both batch and streaming data pipelines. Guarantee the accuracy, integrity, and reliability of extensive data platforms. Establish and enforce stringent quality standards, robust monitoring systems, and effective CI/CD practices for data workflows. Partner with cross-functional teams to deliver solutions that are both scalable and rigorously testable. Oversee data quality programs from initial planning through successful execution. Conduct code reviews, provide mentorship to engineers, and actively promote engineering best practices. Evaluate and integrate tools designed to enhance automation, broaden coverage, and improve observability. Support Agile methodologies and offer technical leadership across a spectrum of projects. Proactively identify opportunities to refine system performance, scalability, and overall quality.

Demonstrate significant experience in Data Quality Engineering, Data Engineering, or Software Testing. Possess advanced proficiency in SQL and Python, specifically for validating large-scale datasets. Showcase hands-on experience with data platforms such as Snowflake, Databricks, Spark, Airflow, or comparable technologies. Present a proven history of developing and scaling automated data testing frameworks. Exhibit a strong background in architecting quality strategies for intricate data pipelines. Apply robust debugging, analytical, and problem-solving skills to complex challenges. Evidence experience in leading technical projects and mentoring engineering teams. Familiarity with cloud data ecosystems, including AWS, is highly desirable. Showcase knowledge of data quality tools like Great Expectations, Deequ, or similar frameworks. A Bachelor's degree in Computer Science or equivalent practical experience is required.