About the Role
The Senior Data Quality Engineer (P3) is responsible for ensuring the reliability, accuracy, and quality of large-scale data platforms across analytics, advertising, and commerce domains. This role designs and implements automated data validation solutions, strengthens data quality practices, and partners with engineering teams to prevent defects before production. The ideal candidate combines strong data engineering knowledge with automation expertise to deliver scalable and maintainable quality solutions.
Responsibilities
- Design, develop, and maintain automated testing solutions for data pipelines and ETL workflows.
- Validate data quality, business logic, KPIs, and metrics across data platforms.
- Perform testing of Snowflake, Databricks, Spark, Airflow, and related data systems.
- Collaborate with Data Engineers, Analysts, Product Managers, and Engineering teams to ensure data accuracy and completeness.
- Develop reusable test automation frameworks that improve scalability and reduce regression risk.
- Identify, troubleshoot, and resolve data inconsistencies, anomalies, and pipeline failures.
- Review code and contribute to quality improvements across data engineering initiatives.
- Support CI/CD processes and ensure quality gates are integrated into deployment workflows.
- Participate in architecture discussions and promote best practices for data quality and testing.
- Drive continuous improvements in automation, coverage, and engineering efficiency.
Requirements
- Strong experience in Data Quality Engineering, Data Testing, ETL Validation, or Data Engineering.
- Advanced proficiency in SQL with experience analyzing and validating large-scale datasets.
- Strong programming skills in Python and test automation development.
- Hands-on experience with Snowflake, Databricks, Hive, Spark, Airflow, or similar modern data platforms.
- Experience validating complex data pipelines, data warehouses, and business-critical reporting systems.
- Knowledge of automation frameworks, CI/CD practices, and quality engineering methodologies.
- Strong debugging, analytical, and problem-solving capabilities.
- Ability to work effectively with cross-functional teams and stakeholders.
- Bachelor's degree in Computer Science or equivalent practical experience.
- Preferred experience with AWS, BDD frameworks (Behave), and data quality tools such as Great Expectations or Deequ.
Success at P3
- Data Quality Excellence: Consistently delivers reliable and accurate validation of data pipelines and business logic.
- Automation Growth: Expands automated coverage through scalable and reusable frameworks.
- Cross-Functional Impact: Influences quality across Engineering, Data, and Product teams.
- Technical Ownership: Leads quality initiatives and drives continuous framework improvements.
- Business Value: Reduces production defects and increases confidence in data-driven decision-making.