Drive data integrity and excellence as a Senior Data Quality Engineer. You will be instrumental in ensuring the reliability and accuracy of vast data platforms spanning analytics, advertising, and commerce. This role involves pioneering automated data validation solutions, enhancing existing data quality frameworks, and proactively collaborating with engineering teams to preempt issues before they reach production.

Success in this position requires a strong foundation in data engineering principles coupled with robust automation expertise. The goal is to deliver high-quality, scalable, and maintainable solutions that support critical business functions. You will be a key player in fostering a culture of quality across the organization.

Design, build, and maintain automated testing solutions for data pipelines and ETL processes. Validate data quality, business logic, key performance indicators (KPIs), and metrics across diverse data platforms. Conduct testing on Snowflake, Databricks, Spark, Airflow, and related data systems. Partner with Data Engineers, Analysts, Product Managers, and broader Engineering teams to guarantee data accuracy and completeness. Develop versatile test automation frameworks that boost scalability and mitigate regression risks. Identify, troubleshoot, and resolve data inconsistencies, anomalies, and pipeline failures. Review code and contribute to overall quality improvements within data engineering initiatives. Support CI/CD pipelines and ensure quality gates are integrated into deployment workflows. Participate in architectural discussions and advocate for best practices in data quality and testing. Champion continuous enhancements in automation, test coverage, and engineering efficiency.

Possess strong experience in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering. Demonstrate advanced proficiency in SQL, with a proven track record of analyzing and validating large-scale datasets. Exhibit strong programming skills in Python and extensive experience in test automation development. Have hands-on experience with modern data platforms such as Snowflake, Databricks, Hive, Spark, or Airflow. Experience validating intricate data pipelines, data warehouses, and business-critical reporting systems is essential. Familiarity with automation frameworks, CI/CD practices, and quality engineering methodologies. Possess robust debugging, analytical, and problem-solving capabilities. Ability to collaborate effectively with cross-functional teams and stakeholders. 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 such as Great Expectations or Deequ.