The QE Lead - Automation Data Migration will lead testing and automation activities for enterprise data migration initiatives. The role is responsible for migration test strategy, automated source-to-target validation, data reconciliation, and ETL/ELT pipeline testing, with a strong focus on data quality, migration readiness, and production validation.

Scope of Work

Automation Data Migration Testing

•       Develop and execute migration test strategies, plans, and validation approaches.

•       Validate source-to-target data mappings and transformation rules.

•       Perform data reconciliation before and after migration.

•       Verify the completeness, accuracy, consistency, and integrity of migrated data.

•       Execute mock, dress rehearsal, and production migration validation cycles.

•       Support cutover, rollback, and migration-readiness activities.

•       Identify and analyze data-quality issues, duplicates, truncation, and transformation defects.

Automation Development

•       Build automated validation frameworks for data migration testing.

•       Develop scripts to compare source and target systems.

•       Automate record-count validation, field-level validation, and business-rule verification.

•       Automate reconciliation reporting and exception-handling processes.

•       Create reusable utilities for large-scale data verification.

Database and ETL Validation

•       Validate ETL/ELT workflows and data pipelines.

•       Test data extraction, transformation, and loading processes.

•       Verify schema changes, stored procedures, jobs, and data-movement processes.

•       Perform auditing and lineage validation across systems.

Required Skills and Experience

Automation and Programming

•       Strong programming and automation experience using Python and/or Java.

•       Strong SQL automation and scripting experience.

Data Validation and Reconciliation

•       Experience building or using automated data-comparison frameworks.

•       Strong experience validating large datasets.

•       Hands-on record-level reconciliation experience.

•       Experience with data profiling and data-quality assessment.

•       Strong understanding of source-to-target validation and migration-testing practices.

Database Technologies

•       Hands-on experience with Oracle.

•       Hands-on experience with SQL Server.

•       Hands-on experience with MySQL.

•       Hands-on experience with Snowflake.