Position: Sr. ETL Tester with AI testing Experience
Location: Chicago, IL and Irving, TX
Duration: Contract opportunity
Job Description:
We are looking for an experienced ETL Tester with knowledge of AI-powered testing tools and Generative AI to validate data pipelines, data transformations, and data migration processes. The candidate will be responsible for ensuring data accuracy, completeness, consistency, and integrity across source and target systems.
The ideal candidate should have strong SQL skills, experience with ETL testing tools, data warehouse concepts, and the ability to use AI tools to improve test case generation, test automation, defect analysis, and testing efficiency.
Key Responsibilities1. ETL Testing and Data Validation
Understand business requirements, source-to-target mapping documents, and data transformation logic.
Perform ETL testing, data migration testing, and data warehouse testing.
Validate data completeness, accuracy, consistency, and integrity between source and target systems.
Test data extraction, transformation, and loading processes.
Validate incremental loads, full loads, CDC (Change Data Capture), and data reconciliation.
Identify data mismatches, duplicate records, missing records, and transformation defects.
2. SQL and Database Testing
Write complex SQL queries using joins, subqueries, CTEs, and window functions.
Perform source-to-target data validation using SQL.
Validate aggregations, business rules, lookup logic, and transformation calculations.
Execute record-count checks, duplicate checks, null checks, and referential integrity validations.
Analyze query results and investigate data discrepancies.
3. AI and Generative AI in Testing
Use AI tools such as ChatGPT, Microsoft Copilot, or approved enterprise AI assistants to generate SQL queries and test scenarios.
Generate test cases and test data from business requirements and mapping documents.
Use AI to analyze transformation logic, identify edge cases, and suggest data-quality validations.
Develop or enhance Python scripts for automated data validation with AI assistance.
Use AI to summarize test execution results, analyze defects, and assist with root-cause analysis.
Evaluate AI-generated test cases and SQL queries for correctness, security, and business-rule compliance.
Explore AI-assisted test automation and intelligent data-quality monitoring.
4. Test Automation and Reporting
Automate repetitive ETL validation tasks using Python, SQL, or relevant testing frameworks.
Execute test cases, document defects, and track issues through closure.
Prepare test plans, test scenarios, test execution reports, and traceability matrices.
Collaborate with developers, data engineers, business analysts, and data architects.
Support regression testing, system integration testing, and user acceptance testing.
