ETL Test engineer

About the Role

Are you passionate about data integrity and building robust, scalable data systems? We are looking for an experienced ETL Test Engineer to validate enterprise data pipelines, data warehouse solutions, and data quality across complex systems.

In this role, you will work closely with our Data Engineering and Analytics teams to ensure seamless data extraction, transformation, and loading (ETL) processes, maintaining the highest standards of data accuracy and performance in Snowflake .

Key Responsibilities

• ETL & DWH Validation: Design, develop, and execute comprehensive test cases for complex ETL workflows and Data Warehouse architectures.
• Source-to-Target Reconciliation: Perform deep source-to-target (S2T) data validation, mapping verification, and data reconciliation across multiple data sources.
• Advanced Querying: Write complex SQL queries (CTEs, window functions, aggregations) to validate data completeness, accuracy, and integrity.
• Snowflake Testing: Validate data pipelines and storage solutions built within Snowflake data warehouses.
• Defect Triage & Troubleshooting: Identify, isolate, track, and troubleshoot data anomalies, drops, and edge-case defects.
• End-to-End Quality Assurance: Support regression, integration, and end-to-end data pipeline testing across production and non-production environments.

Must-Have Qualifications

• Strong, hands-on experience in ETL Testing and Data Warehouse Quality Assurance .
• Practical working knowledge of Snowflake (query optimization, schema structure, data staging).
• Mastery of Advanced SQL for writing complex validation scripts and data checks.
• Deep experience in Source-to-Target (S2T) validation and data reconciliation methodologies.
• Strong knowledge of data quality principles (completeness, consistency, accuracy, timeliness).
• Experience with defect tracking tools (Jira, Azure DevOps, or similar).

Nice-to-Have Skills

• Experience with data testing/validation tools (e.g., QuerySurge, Great Expectations, dbt tests).
• Basic scripting skills in Python or Shell for automated data validation.
• Exposure to Cloud Data Ecosystems (AWS, Azure, or GCP).