Data QA

End-to-End Data Testing & Validation: Conduct hands-on manual data testing and validation across the entire data pipeline-from raw channel ingestion to final marketing data models.

Upstream & Downstream Integration Testing: Validate multi-channel CRM, transaction, and behavioral data flowing into the Data Lake, ensuring the Single Customer View (SCV) model accurately feeds downstream MarTech platforms.

Data Model & Schema Impact Analysis: Introduce new multi-channel data sources (store apps, websites, e-commerce marketplaces, offline POS) while verifying that Medallion data models (Bronze, Silver, Gold) remain unimpacted.

Data Lake Pipeline Verification: Verify ETL/ELT logic, data transformations, and schema consistency across Medallion layers in a Databricks environment.

Cross-Functional Collaboration: Partner closely with global delivery teams (India/US/HK), data engineers, and marketing stakeholders to ensure data accuracy for campaign targeting and CRM execution.

Experience: 5+ years minimum in a Data Engineering, Data Analyst, or Data-focused QA role.

Data Engineering Competency: Hands-on experience with Databricks, Data Lake solutions, and Medallion architecture (Bronze, Silver, Gold layers).

Technical Skills: Strong Python scripting for data validation/querying and advanced SQL skills for deep data profiling.

Data Pipeline Knowledge: Solid understanding of ETL/ELT pipelines, data integration, dimensional modeling, and schema relationships.

Testing Focus: Heavy experience in manual data testing, regression testing, and data model validation (over standard front-end or automated UI testing).

MarTech / CRM Domain: Prior exposure to customer data platforms, MarTech tools, CRM data, or e-commerce/retail transaction pipelines is a strong advantage.