About the Role
The Lead Data Quality Engineer (P4) drives quality strategy and automation across data, analytics, and commerce platforms. This role is responsible for building scalable data quality frameworks, ensuring reliability of data pipelines, and leading quality initiatives that improve trust, performance, and customer experience. The position partners closely with Engineering, Product, and Data teams to establish testing standards, automation solutions, and engineering best practices.
Responsibilities
- Lead data quality strategy, architecture, and automation initiatives.
- Design and implement testing frameworks for batch and streaming data pipelines.
- Ensure accuracy, integrity, and reliability of large-scale data platforms.
- Define quality standards, monitoring, and CI/CD practices for data workflows.
- Collaborate with cross-functional teams to deliver scalable and testable solutions.
- Drive data quality programs from planning through execution.
- Review code, mentor engineers, and promote engineering best practices.
- Evaluate and implement tools that improve automation, coverage, and observability.
- Support Agile delivery and provide technical leadership across multiple projects.
- Identify opportunities to improve system performance, scalability, and quality.
Requirements
- Strong experience in Data Quality Engineering, Data Engineering, or Software Testing.
- Advanced proficiency in SQL and Python for large-scale data validation.
- Experience with Snowflake, Databricks, Spark, Airflow, or similar data platforms.
- Proven track record building and scaling automated data testing frameworks.
- Experience designing quality strategies for complex data pipelines.
- Strong debugging, analytical, and problem-solving skills.
- Experience leading technical initiatives and mentoring engineers.
- Familiarity with AWS or cloud-based data ecosystems.
- Knowledge of data quality tools such as Great Expectations, Deequ, or similar frameworks.
- Bachelor's degree in Computer Science or equivalent practical experience.
Success at P4
- Scalable Data Quality Platforms that improve reliability and trust in data.
- Automation-First Approach reducing manual validation efforts.
- Strong Engineering Standards across testing, monitoring, and CI/CD.
- Technical Leadership that elevates team capability and delivery quality.
- Business Impact through improved data accuracy, performance, and customer experience.