As a Senior Data Quality Engineer, you will be instrumental in ensuring the integrity and accuracy of extensive data platforms supporting analytics, advertising, and commerce.

This role focuses on designing and implementing robust automated data validation solutions. You will enhance existing data quality practices and collaborate closely with engineering teams to proactively prevent defects before they reach production.

The ideal candidate possesses a strong foundation in data engineering complemented by expertise in automation, enabling the delivery of scalable and maintainable quality solutions.

Key responsibilities include designing, developing, and maintaining automated testing for data pipelines and ETL workflows. You will validate critical data quality, business logic, KPIs, and metrics across various data platforms.

Perform hands-on testing of modern data systems like Snowflake, Databricks, Spark, and Airflow. Collaborate effectively with Data Engineers, Analysts, Product Managers, and Engineering teams to guarantee data accuracy.

Develop reusable test automation frameworks to boost scalability and minimize regression risks. Identify, troubleshoot, and resolve data inconsistencies, anomalies, and pipeline failures. Review code and contribute to overall quality enhancements within data engineering initiatives.

Support CI/CD processes, integrating quality gates into deployment workflows. Participate in architectural discussions, championing best practices for data quality and testing. Drive continuous improvements in automation, coverage, and engineering efficiency.

A strong background in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering is essential.

Advanced proficiency in SQL is required, with proven experience in analyzing and validating large-scale datasets. You must possess strong programming skills in Python and a solid track record in test automation development.

Demonstrate hands-on experience with modern data platforms such as Snowflake, Databricks, Hive, Spark, or Airflow. Experience in validating complex data pipelines, data warehouses, and critical business reporting systems is expected.

Familiarity with automation frameworks, CI/CD practices, and quality engineering methodologies is crucial. Excellent debugging, analytical, and problem-solving capabilities are necessary. The ability to collaborate effectively with cross-functional teams and stakeholders is also vital.

A Bachelor's degree in Computer Science or equivalent practical experience is required. Preferred experience includes AWS, BDD frameworks like Behave, and data quality tools like Great Expectations or Deequ.