Capgemini: Data Test Automation Engineer

Join Capgemini as a Data Test Automation Engineer designing, implementing, and scaling automated testing frameworks across enterprise cloud data platforms and modern analytical architectures. In this specialized engineering role, you will be responsible for ensuring data integrity, ETL pipeline accuracy, and seamless transformation logic across complex data ingestion workflows.

You will collaborate closely with data engineers, cloud architects, business intelligence developers, and agile delivery leads to validate high-throughput data pipelines built on Microsoft Azure, Databricks, and Apache Spark. Your technical expertise will directly guarantee data accuracy, accelerate continuous integration workflows, and deliver trusted data foundations for multinational enterprise clients.

Roles & Responsibilities:
• Design, build, and maintain automated test frameworks for ETL/ELT pipelines, data integration workflows, and data platform services.
• Validate end-to-end data ingestion, complex transformations, and target loading across distributed data lakes and data warehouses.
• Develop and execute automated test scripts using Python, PySpark, and SQL to perform source-to-target reconciliation and schema validation.
• Create and maintain automated test suites for Azure Data Factory (ADF) pipelines and Azure Databricks analytical workflows.
• Implement automated data quality checks, reconciliation routines, and anomaly detection rules across large-scale transactional datasets.
• Configure and support CI/CD pipeline automation for continuous data testing using Azure DevOps, Git, and automated testing runners.
• Partner with data governance teams to implement synthetic test data generation, data masking protocols, and regulatory compliance checks.
• Manage test artifacts, test cases, and execution logs using enterprise test management platforms such as Xray and Jira.
• Conduct thorough defect triage, perform root-cause investigations on pipeline data errors, and collaborate with data engineers on fixes.

Requirements & Eligibility:
• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or an equivalent technical field.
• 4+ years of dedicated professional experience in data testing, ETL quality engineering, and data platform test automation.
• Advanced proficiency in SQL with the ability to author complex multi-table analytical queries, joins, and data reconciliation logic.
• Strong programming and scripting skills in Python applied to automated data validation, comparison, and reporting.
• Hands-on testing experience within modern cloud data ecosystems, specifically Microsoft Azure (Azure Data Factory, Azure Databricks, ADLS).
• Solid understanding of distributed computing concepts, Apache Spark / PySpark data processing, and cloud data warehousing.
• Proven track record in designing modular, maintainable automated data validation suites integrated into CI/CD pipelines (Azure DevOps/Git).
• Experience with test management and defect tracking tools including Xray, Jira, and Confluence within agile frameworks.
• Strong analytical reasoning, data intuition, and problem-solving skills with meticulous attention to data consistency and edge cases.
• Excellent English communication skills with the ability to articulate technical data quality findings clearly to multi-functional stakeholders.

Expected Salary:
The estimated compensation bracket for a Data Test Automation Engineer at Capgemini typically ranges between ₹12,00,000 and ₹22,00,000 per annum, benchmarked against verified compensation data from AmbitionBox and Glassdoor. Total compensation packages also include annual performance bonuses, comprehensive health insurance, and structured professional upskilling programs.