Champion data integrity and precision across analytics, advertising, and commerce domains. This role focuses on building and deploying automated data validation solutions, elevating data quality standards, and proactively collaborating with engineering teams to preempt issues before they reach production. The ideal candidate possesses a robust background in data engineering complemented by significant automation expertise to craft solutions that are both scalable and easily maintainable.

Develop, implement, and manage automated testing frameworks for data pipelines and ETL processes.

Verify the accuracy of data, business logic, KPIs, and metrics across various data platforms.

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

Collaborate with Data Engineers, Analysts, Product Managers, and other engineering functions to ensure comprehensive data accuracy and completeness.

Engineer reusable test automation frameworks designed to enhance scalability and minimize regression risks.

Identify, diagnose, and resolve data inconsistencies, anomalies, and pipeline malfunctions.

Contribute to code reviews and enhance overall quality within data engineering initiatives.

Support CI/CD pipelines, ensuring quality gates are seamlessly integrated into deployment workflows.

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

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

Possess substantial experience in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering.

Exhibit advanced proficiency in SQL, with a proven track record of analyzing and validating large-scale datasets.

Demonstrate strong programming capabilities in Python, including expertise in test automation development.

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

Experience validating intricate data pipelines, data warehouses, and critical business reporting systems.

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

Possess strong debugging, analytical, and problem-solving aptitudes.

Effectively collaborate with cross-functional teams and stakeholders.

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

Preferred: Experience with AWS, BDD frameworks (like Behave), and data quality tools such as Great Expectations or Deequ.