QualityKiosk Technologies - Lead Data Test Engineer

Description :

About the job :

Technical Strategy & Enablement :

- Design and implement a Standardized Data Validation Framework (SQL/Python) that can be applied across different sources and pipelines.

- Establish validation logic for ETL/ELT processes, ensuring data lineage is preserved.

- Automated approach for repetitive reconciliation tasks (Source-to-Target) to reduce manual effort and increase team throughput.

Delivery & Quality Governance :

- Act as the Final Technical Sign-off for test plans and results across multiple projects.

- Perform root-cause analysis (RCA) on data discrepancies and schema drifts.

- Ensure all testing activities comply with customers' requirements as per Requirement specification.

- Should have proper understanding of QA project documentation and dependency.

Team Mentorship & Resource Levelling :

- Provide technical guidance to 4-5 Junior Testers, performing code reviews of their SQL/scripts.

- Ensure their doubts are handled in time to reduce invalid bugs during testing.

Stakeholder Management :

- Interface with Client Data Leads and Architects to understand pipeline build and upcoming changes.

- Translate technical data risks into business impact reports for the Project / Program Manager.

- Attend and handle Daily calls for ongoing projects for status updates and technical support along with test engineers

Required Skills & Qualifications :

Experience : 5-7 years in Data Testing/Engineering, with at least 3 years in a Lead role (preferably in Fintech or Banking).

- Highly process oriented and lead 3-4 projects at once.

Technical Stack :

- Expert Level : Advanced SQL (Window functions, complex joins, stored procedures).

Data Pipelines :

- Deep understanding of ETL tools (Confluent Kafka, BODs, or Azure Data Factory) and Data Warehousing (Snowflake ,Databricks or ODS).

Automation :

- Proficiency in Python or Java for data testing automation; experience with any Data quality testing tool is a plus.

Domain :

- Strong grasp of Banking Data Domains (e.g., Credit Risk, Retail Banking, or Regulatory Reporting)