ETL Tester Engineer

The Team

Deloitte's Technology & Transformation practice can help you uncover and unlock the value buried deep inside vast amounts of data. Our global network provides strategic guidance and implementation services to help companies manage data from disparate sources and convert it into accurate, actionable information that can support fact-driven decision-making and generate an insight-driven advantage. Our practice addresses the continuum of opportunities in business intelligence & visualization, data management, performance management and next-generation analytics and technologies, including big data, cloud, cognitive and machine learning.

Your work profile

6+ years experienced Lead testing and quality assurance engineer for ETL pipelines, data platforms, and cloud-based data engineering solutions.

Drive test strategy, automation, data validation, and stakeholder engagement across data transformation initiatives.

Key skills required

• 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 evidnce, automation reports, and dashboards for traceability and coverage metrics.

Must Have

• 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).

Education

Bachelor's degree in computer science, Information Technology, or related field.