About the Role
We are looking for a QA Engineer to lead quality assurance activities across complex, multi-phase cloud data platform delivery. This role covers system and integration testing, parallel-run validation, UAT coordination, and regression testing across all data domains, dashboards, and exception management workflows - ensuring all deliverables meet accuracy thresholds and compliance sign-off requirements before production deployment.
Key Responsibilities
Design and execute system and integration test plans covering data pipeline flows, transformation layers, evaluation engine outputs, and exception workflows
Perform end-to-end pipeline validation across all data domains, verifying data quality gate pass rates and schema conformance
Conduct parallel-run comparisons between automated system outputs and manual reference reports, targeting minimum 90% agreement thresholds
Author UAT test scripts and coordinate sign-off across multiple stakeholder groups including operations, compliance, executive, and audit teams
Validate dashboard accuracy against known control outcomes and verify KPI calculations across all reporting layers
Verify exception and alert workflow behavior including automated ticket creation, SLA countdown triggers, and escalation patterns
Log and manage defects in Azure DevOps, providing root-cause analysis for pipeline discrepancies and evaluation logic issues
Support regression testing activities across all domains following each production deployment
Conduct production smoke testing across all dashboards, pipelines, and integrations prior to formal go-live sign-off
Collaborate with Data Engineers, Technical Leads, and business stakeholders throughout QA cycles
Required Skills & Experience
5+ years of experience in QA or testing roles within data platform, ETL, or cloud analytics projects
Experience designing and executing test plans for data pipelines, including schema validation, transformation accuracy, and data quality checks
Familiarity with Azure DevOps for test case management, defect logging, and release tracking
Experience with parallel-run or reconciliation testing methodologies, comparing automated outputs against reference datasets
Ability to interpret SQL query results and validate data transformation logic across layered data architectures
Exposure to Power BI or BI dashboard validation including KPI accuracy checks and visual consistency reviews
Understanding of UAT coordination processes and stakeholder sign-off workflows across persona groups
Strong analytical skills with attention to detail and ability to identify root-cause discrepancies in complex data flows
Good communication skills and ability to document findings clearly for both technical and business audiences
