Description:

Capgemini is hiring a Senior Software Quality Engineer specializing in ETL and Python Testing across its primary Indian technology hubs. Positioned within the core Quality Engineering (QE) practice, this role focuses on validating complex big data pipelines, financial reporting matrices, and advanced backend architectures. Moving past manual dataset checks, you will design, execute, and scale modular, script-based test automation frameworks. The day-to-day scope balances programming data validation routines, tracking data ingestion logic, scheduling cron scripts inside server environments, and integrating automated evaluation suites directly into continuous integration workflows to guarantee complete data accuracy.

Key Technologies:

Python, Robot Framework, ETL Testing, SQL, Unix/Linux, Jenkins, Git, GitHub Actions, JIRA, qTest

Requirements:

• 4+ years of dedicated professional experience in software quality engineering, specializing heavily in Python-driven testing frameworks and large-scale data validation.
• Proficient in designing, building, and maintaining robust automation frameworks using Python and Robot Framework for keyword-driven and data-driven testing pipelines.
• Hands-on mastery of End-to-End ETL/ELT pipeline testing—including verifying source-to-target mapping profiles, asserting complex transformation math, validating boundary parameters, and scanning metadata layer constraints.
• Strong data querying skills utilizing advanced SQL architectures to construct automated backend validation scripts, test data quality exceptions, and audit analytical reporting structures.
• Direct capability navigating terminal environments, executing shell commands, and managing, running, or monitoring automation jobs and scheduled tasks.
• Solid working knowledge embedding automated test routines inside modern CI/CD grids using Jenkins, Git, GitHub Actions, UrbanCode Deploy (UCD), or Helios.
• Functional understanding of corporate data processing, complex data workflows, business intelligence (BI) concepts, and practical exposure to foundational statistics or data science principles.
• Day-to-day operational familiarity using project tracking and test lifecycle documentation tools, specifically JIRA, Confluence, and qTest.
• Deep analytical problem-resolution skills, strong critical thinking to map out data edge-cases, high execution ownership inside Agile sprint frameworks, and clear communication habits to coordinate with distributed global development teams.