Lead QA Engineer: Financial Data Quality

IBM; Boston (MA); Remote; USD 114,000 - 212,000; Full time

Job summary

IBM Consulting in the United States seeks a QA Engineer - Financial Systems to design and execute comprehensive data quality testing strategies for ETL, Big Data, and data-intensive applications, including BAO, MDM, and data migrations.

You will plan testing activities, estimate work efforts, create test cases, and identify test environment and data requirements, while using manual and automation solutions to ensure data quality and integrity.

Qualifications

• Bachelor's degree required in a relevant field.
• Experience designing data quality tests for ETL, Big Data, and data migrations.
• Ability to understand business requirements and design optimized test scenarios.

Responsibilities

• Design Testing Strategies: Design and execute comprehensive data quality testing strategies for ETL, Big Data, and data-intensive applications, including BAO, MDM, and data migrations.
• Plan Testing Activities: Plan and execute testing activities, estimate work efforts, create test cases, and identify test environment and test data requirements.
• Execute Test Cases: Utilize manual and automation solutions to execute test cases, report test status, and perform defect creation, triage, and resolution.
• Analyze Test Outcomes: Analyze test outcomes for continuous improvements using analytical and cognitive techniques and tools.
• Optimize Test Scenarios: Develop a deep understanding of business requirements and design optimized test scenarios to ensure data quality and integrity.

Skills

Data quality testing

ETL

BAO

MDM

Data migrations

Test planning

Test automation

Test analysis

Analytical skills

Education

Bachelor's Degree

Job description

IBM Consulting in the United States seeks a QA Engineer - Financial Systems to design and execute comprehensive data quality testing strategies for ETL, Big Data, and data-intensive applications, including BAO, MDM, and data migrations.

You will plan testing activities, estimate work efforts, create test cases, and identify test environment and data requirements, while using manual and automation solutions to ensure data quality and integrity.