Senior QA Testing Specialist Data Engineer

Role Description

Role Objectives

Data Quality & Validation:

• Perform endtoend data validation including sourcetotarget mapping reconciliation and completeness checks. Validate data transformations across Databricks Medallion Architecture (Bronze Silver Gold).
• Ensure data quality standards are met prior to production releases.

Test Strategy & Execution:

• Participate in requirements analysis and solution design reviews.
• Define test scope scenarios riskbased coverage and overall test strategies.
• Design and execute test cases for complex ETL pipelines and Perform regression testing and manage test data conditions.

DataValidation & Automation:

• Write and execute advanced SQL queries (CTEs window functions complex joins).
• Develop Python validation scripts using PySpark and Pandas to support and enhance data test automation efforts where applicable.
• Monitor Databricks jobs and workflow executions. Investigate pipeline failures data inconsistencies and quality issues. Analyze defects both functionally and technically prior to assignment.

Defect Management & Collaboration:

• Log track and manage defects using JIRA ensuring highquality defect documentation.
• Collaborate closely with Data Engineers Architects and Business teams to resolve issues.
• Provide clear and timely communication on quality risks test progress and release readiness and Escalate issues through defined governance and quality escalation channels.

Qualifications and Skills

• 8 yearsof experience inQuality Assurance testingor relevantData Engineering developmentroles.
• DemonstratedexpertiseinAdvanced SQL includingCTEs window functions and complex joins forlargescaledata validation.
• 6 years of experience in test management strategy; experience in the securities or financial services industry
• 3 years of experience in designing solutions in process flow businesslogicand UI.
• Strong experience within theAzure data ecosystem withhandsonexpertiseinDatabricks notebooks and cluster management.
• Experiencebuilding or supporting data test automation frameworks with exposure toCI/CD pipelines andGitbasedversion control.
• Hands-on manual and automated test development and design experience leading medium to large scales or enterprise-wide projects and production implementations.
• Strong command ofPython particularlyPySparkand Pandas for comprehensive data validation and analysis.
• Thorough understanding ofETL processes data pipelines and data warehouse architecture with proven experience inETL/Data Warehouse testingincludingendsourcetotargetvalidation.
• Prior experience working directly withbusiness users and finance stakeholders including exposure toOracle or similar enterprise financial platforms.
• Stronganalyticalcriticalthinking andproblemsolvingskills.
• Excellentwritten and verbal communicationskills with the ability to articulate technical concepts clearly.
• Ability to workindependentlywhile collaborating effectively acrossdistributed and global teams.

Measures of Success

• High levelof confidence in production data quality reflected by reduced defect leakage postrelease.
• Strong and consistent test coverage across complex multilayer data platforms.
• Effective collaboration with engineering product and business teams throughout the delivery lifecycle.
• Proactive identification and mitigation of data quality risks and issues.
• Consistent ontime delivery of test outcomes without compromising quality standards.