Engineering & Data Pipeline DevelopmentContribute to the design, development, and enhancement of ETL/data engineering pipelineson GCP (BigQuery, DataProc, Cloud Composer).Build and optimize data transformations, ingestion frameworks, and pipeline components using Python and PySpark.Ensure pipelines are scalable, efficient, and aligned with best engineering practices. Quality Engineering & Automation Design and develop scalable data validation frameworksand automation utilities.Implementend-to-end data quality checks including reconciliation, schema validation, and business rule enforcement. Build and maintain automated test suitesfor functional, regression, and data validation scenarios.Integratedata quality checks into CI/CD pipelinesto enable automated quality gates. Platform Reliability & Observability: Ensure high availability, reliability, and performance of data pipelines.Implementmonitoring, alerting, and data observability mechanisms.Performroot cause analysis (RCA)and implement preventive solutions for recurring issues
