Innocito
Website:
innocito.com
Job details:
Title: Senior/Lead ETL QA Tester
Location: Hyderabad
Full time
Position Overview
We are seeking a single QA Analyst who can support both our BI reporting and Data Engineering/ETL validation needs.
The workload in these areas is not high, but we need someone who can cover the full spectrum of testing: report validation, data accuracy, pipeline checks, and basic automation.
The ideal candidate will be comfortable working across both domains and collaborating closely with the developers and project leads.
Data Engineering and Legacy Reporting are functions of my Data Engineering group.
BI Testing Responsibilities.
Primary Skills- ETL Testing, Data Validation, Power BI Reporting, Azure data ecosystem.
Requirements & Test Planning:
Review Jira user stories, acceptance criteria, specifications, and mockups.
Develop comprehensive test cases to ensure all functional and data requirements are validated.
Report & Data Validation:
Validate data and calculations using SQL, Power Query, and DAX.
Ensure BI reports reflect accurate data flow from source systems through the expected transformations.
Visuals & Functional Testing:
Test report functionality including drill-through, filters/slicers, tooltips, bookmarks, and interactive behaviors.
Validate data refreshes, connectivity, and consistency across environments.
Performance & Security:
Test performance metrics such as report load time and interaction responsiveness.
Verify security configurations, particularly row-level security (RLS).
Defect Management & Regression Testing:
Log and maintain Jira bugs with clear documentation.
Work with developers and stakeholders to confirm fixes.
Perform regression testing after updates to ensure no negative impact on existing content.
Data Engineering & Legacy Reporting Responsibilities
ETL/ELT & Pipeline Testing:
Strong SQL skills to validate ETL/ELT workflows and data transformations.
Create both positive and negative test scenarios to ensure data correctness and resilience.
Validate business rules and mapping logic across stages of the pipeline.
Data Reconciliation:
Perform end-to-end data checks and reconciliation from source to target.
Document data discrepancies and work with engineering teams to resolve issues.
Automation & Cloud Tools:
Basic Python for automating data checks (preferred but not required).
Familiarity with Azure data ecosystem—Data Lake, Synapse, and Azure Data Factory.
Documentation & Collaboration:
Produce clear test documentation and validation summaries.
Coordinate closely with engineering and data teams for test planning and issue resolution.
Click on Apply to know more.
