Description

Key Responsibilities
• ✅ Validate ETL/ELT pipelines (source → staging → target)
• ✅ Perform data quality checks (completeness, accuracy, consistency)
• ✅ Design & execute test cases for large datasets
• ✅ Automate testing using SQL, Python, or data QA tools
• ✅ Work with data lakes, warehouses (Snowflake, Redshift, BigQuery, Azure Synapse)
• ✅ Perform data reconciliation and aggregation validation
• ✅ Ensure data governance & compliance standards
• ✅ Collaborate with data engineers, analysts, and business teams
• ✅ Implement CI/CD testing for data pipelines
• validates data + AI/ML models + pipelines, ensuring training data quality, model correctness, and production reliability of AI systems.
AI/ML Knowledge
• ML algorithms (Regression, Classification, Clustering)
• Model evaluation metrics
• Feature engineering concepts
• Training vs inference pipeline

Key Skills

Education

Bachelor's degree