Lead Data Quality Engineer

We are seeking aLead Data Quality Engineer to drive rigorous data validation, SQL-based testing, and quality automation across cloud data environments. You will verify complex transformations, ensure consistency across multiple systems, and support migration and deployment work within modern data ecosystems. Join a distributed team focused on trustworthy datasets and apply today.

Responsibilities

• Execute QA validation for bulk data products within the data exchange ecosystem
• Validate match and append processes and ensure correct deployment into data pipelines
• Provide QA support for data platform migrations, ensuring data integrity and functional correctness
• Verify data products and data fulfillment processes for accuracy and completeness
• Perform data validation and comparisons across systems, including source input files to cloud data warehouse tables, table-to-table checks, and confirmation of data mappings and transformations against specifications
• Use and enhance quality check frameworks built with PySpark scripts to automate data validation
• Investigate defects through data analysis, identifying root causes in data pipelines or transformation logic
• Collaborate with engineering and data teams to triage issues, validate fixes, and confirm production readiness
• Contribute to test automation for data validation and testing to increase efficiency and coverage
• Communicate findings, risks, and test results clearly to stakeholders

Requirements

• 5+ years of experience in Data Quality Engineering
• Expertise in SQL, including complex joins across multiple tables and large datasets
• Hands-on experience with cloud data warehouses such as BigQuery, Redshift, or Synapse Analytics
• Working knowledge of cloud platform services across AWS, Azure, or GCP
• Understanding of data validation practices and automated data comparison techniques
• Background in data transformation, validation, and mapping verification using specifications
• Capability to understand and work with PySpark-based quality frameworks
• Strong data analysis and debugging skills, with an ability to identify defects in data processing pipelines
• Excellent written and verbal communication skills
• Upper-Intermediate English proficiency (B2)

Nice to have

• Proficiency in Python or PySpark development
• Background in data engineering or data pipeline testing environments