Join our Technology & Transformation practice, a team dedicated to unlocking the power of data. We assist companies in managing diverse data sources and transforming them into actionable insights for informed decision-making. Our expertise spans business intelligence, data management, performance management, and cutting-edge analytics including big data, cloud, and AI technologies.

This role focuses on driving quality assurance for ETL pipelines, data platforms, and cloud-based data solutions. You'll be instrumental in shaping test strategies, implementing automation, and ensuring data integrity across critical transformation initiatives.

Key responsibilities include designing and implementing automated test frameworks for ETL/ELT processes. You will validate source-to-target data reconciliation, transformation logic, and business rules, while developing scripts for data completeness and accuracy checks.

Further duties involve performing database, file, and API testing, creating reusable automation components, and executing regression and integration tests. Analyzing test failures, identifying root causes, and validating incremental loads and historical migrations are also crucial. Integrating automation with CI/CD pipelines and preparing comprehensive test documentation are also key aspects of this role.

We are seeking an experienced ETL Testing and Quality Assurance Engineer with 3-6 years of experience. A Bachelor’s degree in Computer Science, Information Technology, or a related field is required.

Essential technical skills encompass a strong understanding of ETL, data warehousing, data lakes, and UNIX. Proficiency in automation tools like Python/PyTest/PySpark/Robot Framework/TestNG, advanced SQL for data validation, and experience with data platforms such as Databricks, Snowflake, Redshift, BigQuery, or Synapse are necessary. Familiarity with ETL tools (Informatica, Ab Initio, Talend, DataStage, dbt) and cloud platforms (AWS, Azure, GCP) is also expected, alongside experience with CI/CD tools and various data formats.