Nishant Agarwal

Designed and Optimized Scalable Data Pipelines: Engineered scalable ETL workflows on Azure (ADF/Synapse) and Databricks, automating ingestion for global datasets and improving throughput by 30%. Built a reusable, high-performance Data Quality Framework tailored for data migration projects, enabling rapid and consistent validation across multiple data sources and targets. Led Global Data Migration Projects: Led cross-continental data migrations to Databricks for global clients. Implemented custom frameworks to replicate legacy systems like Teradata’s functionalities, achieving 43% better performance and 40% cost savings. Enhanced Data Pipeline Efficiency: Tuned Spark configurations and query optimizations, reducing job execution times by 30% and boosting system throughput, ensuring high performance under production workloads. Ensured Data Integrity and Validation: Specialized in data validation during migration projects, ensuring seamless transitions with zero critical failures. Validated data from source to target systems when mismatched by creating custom automation framework. Recognized as the best data validation resource in the company for consistently delivering accurate and r