Test Automation Architect (Data Migration & Snowflake)

We are seeking a strategic and highly technical Test Automation Architect to lead the quality engineering efforts for a large-scale data migration project to Snowflake. You will be responsible for designing a robust, multi-tool automation framework that ensures data integrity, performance, and seamless business process continuity. As the Architect, you will orchestrate the integration of Tricentis Tosca, UFT, and Python to validate complex ETL pipelines and cloud data warehouse architectures., Strategic Framework Design: Define and implement a hybrid automation strategy using Tosca for model-based UI/API testing, UFT for legacy system integration, and Python for high-volume data validation. Snowflake Validation: Develop custom Python-based utilities and libraries to perform automated source-to-target reconciliation, schema validation, and data quality checks within Snowflake. End-to-End Orchestration: Lead the design of automated workflows that bridge the gap between legacy on-premises systems and modern cloud data environments. CI/CD Integration: Embed automated testing suites into the DevOps pipeline to enable continuous testing for ETL jobs and data transformations. Technical Leadership: Provide architectural governance, conduct code reviews for Python scripts, and establish best practices for Model-Based Test Automation (MBTA) in Tosca. Performance & Scalability: Design automated tests to evaluate Snowflake warehouse performance, concurrency, and data throughput.

Cloud Platforms: Expert-level knowledge of Snowflake architecture, including Virtual Warehouses, Stages, Snowpipe, and Secure Data Sharing. Automation Tooling (Tosca): Mastery of Tricentis Tosca, including Distributed Execution (DEX), Test Data Service (TDS), and API scan modules. Automation Tooling (UFT): Advanced proficiency in Micro Focus UFT/One, specifically VBScript scripting, Object Repository management, and legacy application integration. Programming: High proficiency in Python for data engineering tasks, utilizing libraries such as Pandas, PyTest, Great Expectations, and the Snowflake-Python Connector. Data Engineering & ETL: Deep understanding of ETL/ELT lifecycles, data mapping, and validating transformations from RDBMS (Oracle, SQL Server) to Cloud Data Warehouses. Advanced SQL: Mastery of complex SQL for data profiling