Case Study: ETL Testing for mid-size retail bank

by admin on June 22, 2011

The Client: Mid-size retail bank 4th fastest growing company in the world  $1.5B in annual revenue.

Business Need:  ETL QA Engineer to improve the efficiency of the QA organization and increase test coverage of systems that support various business processes.

Our Solution:

SQA Solution partnered with client and assigned a Data warehouse QA Analyst to be responsible for the planning and execution of DW QA processes for all DW related projects. The Data warehouse QA Analyst established and owned QA process, end to end. Our Data warehouse QA have extensive experience in testing Data warehouse functions such as ETL and Reporting, and are subject matter in Data warehouse areas, and have  technicals skills with SQL & PL/SQL and testing tools. Data warehouse QA Analyst is responible for:


– Review design/functional specifications and develop detailed test plans;

– Create and execute test cases for functional, integration, regression, and user acceptance (UAT) testing;

– Develop automated testing scripts;

– Develop tools as well as define test suites and create test data;

– Communicate test results to upper management;

– Track defects from discovery to resolution;

– Work in close cooperation with product owners, developers and release engineers.


Our Data warehouse QA Team posses following skills:


– Strong programming and automation skills in PL/SQL;

– Experience with creating Unix shell scripts;

– Software QA experience, with a distinguished track record on technical projects;

– Data Warehouse QA experience:

  • Batch loads
  • Error/rejected records processing
  • Testing of SCD implementations and process dependencies
  • STM testing

– Hands on experience with Test Automation tools;

– Reporting tools:

  • Business Objects Enterprise
  • Cognos
  • Microstrategy.

– ETL tools:

  • Business Objects Data Integrator
  • Informatica

– Strong knowledge of SQL and PL/SQL;

– Oracle database 9i/10g.


  • Application was released on Time, with in budget and schedule
  • There were not immediate problems reported by users following production roll out
  • Regression Test Cases were automated for future releases


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