Seeking a Senior Data Quality Engineer to elevate the reliability and accuracy of extensive data platforms across analytics, advertising, and commerce. This role is instrumental in designing and implementing sophisticated automated data validation solutions. You will enhance existing data quality practices and collaborate closely with engineering teams to proactively prevent defects, ensuring robust data integrity before production deployment. The ideal candidate possesses a strong blend of data engineering acumen and automation expertise, dedicated to delivering scalable and maintainable quality solutions.

Success in this role is defined by achieving Data Quality Excellence through consistent, accurate validation of data pipelines and business logic. It also involves Automation Growth by expanding automated coverage with scalable, reusable frameworks, and Cross-Functional Impact by influencing quality across Engineering, Data, and Product teams. You will demonstrate Technical Ownership by leading quality initiatives and driving continuous framework improvements, ultimately delivering significant Business Value by reducing production defects and bolstering confidence in data-driven decision-making.

Key responsibilities include designing, developing, and maintaining automated testing solutions for data pipelines and ETL workflows. You will validate critical data quality, business logic, KPIs, and metrics across diverse data platforms. Hands-on testing of Snowflake, Databricks, Spark, Airflow, and associated data systems is essential. Collaboration with Data Engineers, Analysts, Product Managers, and Engineering teams is vital to ensure data accuracy and completeness. Develop reusable test automation frameworks to enhance scalability and minimize regression risk. Identify, troubleshoot, and resolve data inconsistencies, anomalies, and pipeline failures. Review code and contribute to overarching quality improvements in data engineering initiatives. Support CI/CD processes and integrate quality gates into deployment workflows. Participate actively in architecture discussions and champion best practices for data quality and testing. Drive continuous enhancements in automation, coverage, and overall engineering efficiency.

A strong foundation in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering is required. Advanced proficiency in SQL is essential for analyzing and validating large-scale datasets. Robust programming skills in Python and experience in test automation development are crucial. Practical experience with modern data platforms such as Snowflake, Databricks, Hive, Spark, or Airflow is necessary. Proven experience validating complex data pipelines, data warehouses, and business-critical reporting systems is expected. Familiarity with automation frameworks, CI/CD practices, and quality engineering methodologies is beneficial. Exceptional debugging, analytical, and problem-solving capabilities are a must. The ability to collaborate effectively with cross-functional teams and stakeholders is key. A Bachelor's degree in Computer Science or equivalent practical experience is required. Preferred experience includes working with AWS, BDD frameworks like Behave, and data quality tools such as Great Expectations or Deequ.