Join our dynamic team as a Lead Data Quality Engineer (P4) and spearhead the quality strategy for our data, analytics, and commerce platforms. This pivotal role involves developing robust, scalable data quality frameworks and ensuring the utmost reliability of our data pipelines.

You will be instrumental in driving quality initiatives that enhance data trust, boost performance, and elevate the customer experience. By collaborating closely with Engineering, Product, and Data teams, you'll establish rigorous testing standards, implement cutting-edge automation solutions, and champion engineering best practices.

This position offers an exciting opportunity to shape the future of data quality within a growing organization.

Drive the overarching data quality strategy, architecture, and automation efforts.

Design and implement sophisticated testing frameworks for both batch and streaming data pipelines.

Guarantee the accuracy, integrity, and reliability of extensive data platforms.

Establish clear quality standards, implement effective monitoring, and integrate CI/CD practices for data workflows.

Collaborate with cross-functional teams to deliver scalable and thoroughly tested solutions.

Lead data quality programs from initial planning through successful execution.

Conduct code reviews, mentor fellow engineers, and actively promote superior engineering practices.

Evaluate and integrate tools that enhance automation, expand coverage, and improve observability.

Support Agile delivery methodologies and provide essential technical leadership across various projects.

Demonstrate strong experience in Data Quality Engineering, Data Engineering, or Software Testing.

Possess advanced proficiency in SQL and Python for validating large-scale datasets.

Have hands-on experience with data platforms such as Snowflake, Databricks, Spark, Airflow, or comparable technologies.

Show a proven history of building and scaling automated data testing frameworks.

Exhibit experience in designing quality strategies for intricate data pipelines.

Display robust debugging, analytical, and problem-solving capabilities.

Possess experience in leading technical initiatives and mentoring engineering staff.

Be familiar with AWS or other cloud-based data ecosystems.

Have knowledge of data quality tools like Great Expectations, Deequ, or similar frameworks.

Hold a Bachelor's degree in Computer Science or possess equivalent practical experience.