Quality Assurance (QA Engineer)
Core
Validate consumer identity, profile, behavioral, and transactional data across data ingestion, transformation, and analytics layers to ensure data consistency and accuracy.
Role type
QA Engineer (Data Quality & Analytics)
Builds
Consumer analytics datasets, dashboards, and data pipelines for business intelligence.
Domain
Consumer data analytics, data engineering, and business intelligence.
Deliverable
production ML models | product features | dashboards & analysis
Required skills
SQL, data pipeline testing, BI dashboard validation, data modeling (identity resolution, events, transactions), defect tracking, test planning, reconciliation queries, Agile methodologies.
Preferred skills
Tableau, ThoughtSpot, Looker, data quality frameworks, CI/CD pipelines, Git, data governance, privacy compliance (GDPR/CCPA), automation tools (Postman, Pytest).
Technologies
Google BigQuery, Tableau, ThoughtSpot, JIRA, Git, Postman, Pytest
Responsibilities
Perform end-to-end dataset comparisons for consumer records and attributes; reconcile KPIs in dashboards against source tables; test ETL/ELT pipelines; validate business logic for attribution and segmentation; conduct row-level and trend-based testing; partner with data engineers to resolve data issues; create reusable test plans and documentation.
Seniority
Mid-level (2–5 years experience)
