Hybrid
Full Time
• Design and develop Python-based automation frameworks and reusable utilities for ETL validation and data reconciliation.
• Conduct end-to-end ETL testing, including source-to-target validation, transformation logic verification, and data completeness checks.
• Write, review, and optimize SQL queries (joins, CTEs, window functions) to validate complex business rules and data transformations.
• Execute Unix/Linux commands and shell scripts for ETL job monitoring, file validation, and log analysis.
• Work with Mainframe systems for flat file validation, dataset checks, and job executions in ETL processes.
• Test data warehouse components such as staging, fact, and dimension tables, including Slowly Changing Dimensions (SCDs).
• Integrate automated validation into ETL CI/CD pipelines (Jenkins, GitHub Actions).
• Collaborate with ETL developers, data modelers, and business analysts to investigate issues and ensure accurate data flow across banking systems.
• Prepare test evidence, automation reports, and dashboards for traceability and coverage metrics.
• Strong proficiency in Python (pandas, pyodbc, data comparison and validation frameworks).
• Expertise in SQL — complex joins, aggregations, subqueries, and analytical functions.
• Strong understanding of Oracle databases/datawarehouse and relational data modeling.
• Proficiency in Unix/Linux commands (grep, awk, sed, shell scripting).
• Basic knowledge of Mainframe systems for dataset handling.
• Good understanding of Data Warehousing concepts — staging, data marts, fact/dimension modeling, and SCD handling.
• Familiarity with test management tools (Rally, JIRA, Qtest) and CI/CD tools (Git, Jenkins).
Capgemini
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