Shailesh Navendu

Designed and implemented scalable AWS cloud data pipelines with PySpark and Python, integrating heterogeneous data sources into centralized analytics platforms. Optimized distributed processing workloads for cost and performance on Snowflake and Redshift while enforcing data quality, governance, and monitoring.

Led development of a large-scale oncology analytics platform for predictive patient analytics, processing 10TB+ of healthcare datasets. Built AWS Glue/Lambda/Redshift/S3 ETL pipelines and reduced pipeline runtime by 35%, improving orchestration reliability and observability with Apache Airflow.

Built PySpark and SparkSQL pipelines for large-scale financial data processing across loan and card products, ingesting millions of transactions daily. Optimized SQL queries to reduce batch processing time by 40%, supporting SLA compliance and regulatory reporting.

Developed validation frameworks and automated batch processes for enterprise systems handling critical financial records. Optimized SQL and PL/SQL stored procedures to improve pipeline efficiency and maintained 99.9% SLA compliance across production batch flows.

Automated data loading pipelines using Autosys job scheduling, significantly reducing manual intervention. Built PL/SQL procedures, functions, and triggers to support enterprise data transformation workflows.

Designed database schemas and optimized SQL queries for enterprise applications supporting thousands of concurrent users. Developed and maintained stored procedures and database solutions to ensure database performance and reliability.

Completed the Snowflake SnowPro Core certification.

Grade: CGPA: 8.7 / 10

Earned a B.Tech in Electronics & Telecommunication at SASTRA University in 2011, achieving CGPA 8.7/10.

Pursuing the AWS Certified Data Engineer – Associate credential (status: in progress/target).

Completed the Databricks Certified Associate Developer for Apache Spark certification.