- Design scalable PySpark-based test architectures for ETL/data pipelines, including modular frameworks for batch processing.
- Architect end-to-end data validation systems in Hadoop setting for lineage, schema evolution
- Lead system design for Hadoop/Hive test environments, including YARN resource management, energetic partitioning.
- Exposure to Zephyr-Jira-ServiceNow integrated test management systems with experience on API-driven automation.
- Design CI/CD test pipelines for PySpark/Hadoop jobs, incorporating artifact management, parallel execution, and blue-green deployments.
- Create data quality system designs using PySpark integrated with Hive metadata services.
- Design testing platforms, test data generators
- Mentor juniors on PySpark testing basics, contribute to testing strategy discussions
- Spark session configurations for memory and core allocations for both local and cluster manager settings
- Data handling with distributed file systems like HDFS and writing back to hive tables
- Implementation of Partitioning, caching techniques in organizing code for transformation pipelines
- Performance tuning implementation like salting, minimizing shuffling