Descripción del puesto
What you’ll bring: 2+ years in QA, with at least 1 year focused on data or AI pipeline testing. Proficiency in Python for test automation (pytest, custom scripts). Solid SQL skills to validate data across sources. Hands-on experience with data quality frameworks (Great Expectations, dbt tests, Soda, or similar). Strong understanding of ETL/ELT concepts and pipeline architecture. Familiarity with workflow orchestration tools (Airflow or similar). Upper-intermediate English (B2+).
Nice to have: Experience testing ML model outputs (predictions, scoring, drift detection). Familiarity with data warehouses (Snowflake, BigQuery, Redshift). Knowledge of data observability tools (Monte Carlo, Metaplane). Experience with CI/CD integration for data tests (GitHub Actions, GitLab CI).
Design and implement automated tests for data pipelines (ETL/ELT), data transformations, and data quality checks
Validate data integrity, completeness, and consistency across sources and destinations
Test AI/ML pipeline outputs — model predictions, feature engineering logic, and data drift
Write and maintain dbt tests, Great Expectations rules, or similar data validation frameworks
Build and maintain test datasets and test environments
Collaborate with data engineers to identify edge cases and failure points early
Document test plans, test cases, and quality metrics
Participate in incident analysis and root cause investigations for data issues
Requisitos
- Data Test EngineerExperience in data validation at scale and exposure to production-grade data platforms. Ability to translate business requirements into concrete test strategies. Strong communication skills to collaborate with cross-functional teams and to document complex data issues clearly. Proactive mindset for improving data reliability and test coverage across multiple projects.