Advance data integrity and accuracy across analytics, advertising, and commerce platforms as a Senior Data Quality Engineer. This role is pivotal in building and implementing automated data validation solutions, enhancing existing quality protocols, and proactively collaborating with engineering teams to prevent production defects. The ideal candidate possesses a robust blend of data engineering acumen and automation expertise, essential for creating scalable and sustainable quality frameworks.

We are looking for an experienced professional to ensure the reliability of large-scale data systems. Your work will directly impact the quality and trustworthiness of the data that drives critical business decisions.

Key duties include designing, developing, and maintaining automated testing solutions for data pipelines and ETL processes.

You will validate data quality, business logic, KPIs, and metrics across diverse data platforms.

Perform hands-on testing of systems like Snowflake, Databricks, Spark, and Airflow.

Collaborate closely with Data Engineers, Analysts, Product Managers, and wider engineering teams to guarantee data accuracy and completeness.

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

Identify, troubleshoot, and resolve data inconsistencies, anomalies, and pipeline failures.

Contribute to code reviews and drive quality enhancements across all data engineering initiatives.

Support CI/CD pipelines, ensuring quality gates are embedded in deployment workflows.

Participate in architecture discussions, championing best practices for data quality and testing.

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

Essential qualifications include strong experience in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering.

Possess advanced SQL proficiency for analyzing and validating extensive datasets.

Demonstrate strong programming skills in Python, specifically for test automation development.

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

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

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

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

Effectively collaborate with cross-functional teams and stakeholders.

A Bachelor's degree in Computer Science or equivalent practical experience is necessary.

Preferred experience includes working with AWS, BDD frameworks like Behave, and data quality tools such as Great Expectations or Deequ.