ETL Test Engineer Location : Chicago, IL ( Onsite from day 1 )
shivaji D
Hope you are doing great.
Please find the requirements below and send me matched resumes to shi...@nexwaveinc.com
Role : ETL Test Engineer
Location : Chicago, IL ( Onsite from day 1 )
Duration : 12 Months
JD:
Test Engineer – Data Migration & Transformation Validation
• Lead testing efforts for large-scale data transformation and migration initiatives involving millions of records across enterprise Network Operation platforms.
• Design and execute comprehensive data validation strategies to ensure data accuracy, completeness, consistency, and integrity during migrations.
• Develop complex SQL queries and automated validation scripts to reconcile source and target systems at scale.
• Validate ETL/ELT processes, transformation rules, data mappings, and business logic across multiple data domains.
• Perform end-to-end testing of batch, real-time, and incremental data migration workflows.
• Identify, analyze, and troubleshoot data quality issues, migration defects, duplicates, missing records, and transformation anomalies.
• Collaborate closely with data engineers, architects, business analysts, and application teams to define test strategies and acceptance criteria.
• Generate detailed data quality metrics, reconciliation reports, and migration readiness assessments for stakeholders.
• Drive best practices in database testing, automation, and data quality engineering to ensure successful delivery of enterprise migration programs.
AI Agent Onboarding & Test Automation
• Partner with AI agent onboarding partners to define and execute the onboarding approach for all three UA agents.
• Establish an automation-first testing strategy for the product and agent-based workflows.
• Review the product architecture diagrams, and overall solution objectives to understand the end-to-end agent workflow and testing requirements.
• Identify opportunities to use AI-driven test automation to improve test coverage, execution efficiency, and agent workflow validation.
• Collaborate with development, architecture, AI partners, and QA teams to embed DB automation & AI agent into the product development lifecycle.
• Define test scenarios and validation strategies for AI-agent-based workflows, covering functional, integration, data, and end-to-end validation.