Join our dynamic team as a Senior Data Quality Engineer and play a crucial role in upholding the integrity of our extensive data platforms. You will be instrumental in designing and implementing sophisticated automated data validation systems, enhancing our existing data quality protocols, and collaborating closely with engineering teams to proactively identify and resolve issues before they impact production environments. This position requires a unique blend of deep data engineering knowledge and robust automation expertise to build solutions that are both scalable and maintainable, ensuring the highest standards of data reliability across analytics, advertising, and commerce.

This role offers the opportunity to significantly influence data-driven decision-making by ensuring the accuracy and trustworthiness of our data assets. You will be a key contributor to architectural discussions, championing best practices in data quality and testing methodologies.

Develop and maintain advanced automated testing solutions for intricate data pipelines and ETL processes.

Validate the accuracy of data quality, business logic, key performance indicators (KPIs), and metrics across diverse data platforms.

Conduct comprehensive testing on critical data systems including Snowflake, Databricks, Spark, and Airflow.

Collaborate effectively with Data Engineers, Analysts, Product Managers, and broader Engineering teams to guarantee data accuracy and completeness.

Build and enhance reusable test automation frameworks to boost scalability and minimize regression risks.

Proactively identify, troubleshoot, and resolve data inconsistencies, anomalies, and pipeline failures.

Review code contributions and actively promote quality improvements throughout data engineering initiatives.

Support continuous integration and continuous deployment (CI/CD) processes, ensuring quality gates are embedded within deployment workflows.

Engage in architectural discussions, advocating for and promoting best practices in data quality and testing.

Drive ongoing enhancements in automation, test coverage, and overall engineering efficiency.

Demonstrate strong expertise in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering roles.

Possess advanced proficiency in SQL, with proven experience in analyzing and validating large-scale datasets.

Exhibit strong programming skills in Python, particularly in developing test automation solutions.

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

Showcase experience in validating complex data pipelines, data warehouses, and critical business reporting systems.

Understand automation frameworks, CI/CD practices, and established quality engineering methodologies.

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

Effectively collaborate with cross-functional teams and various stakeholders.

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

Preferred experience includes familiarity with AWS, Behavior-Driven Development (BDD) frameworks like Behave, and data quality tools such as Great Expectations or Deequ.