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Remote in Mexico, & 4 others
Data Quality Engineering
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We are seeking a Lead 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