Mohan CH

• Designed and implemented scalable ETL pipelines in Airflow and AWS Step Functions, improving reliability and reducing job failures by 30%.
• Built PySpark jobs on EMR to process 1TB+ structured and semi-structured data daily, reducing processing time from 5 hours to 2 hours.
• Developed AWS Glue jobs for schema discovery, data transformation, and partitioning in S3, cutting query costs by 25%.
• Modeled and optimized Redshift schemas (star and snowflake models), enhancing BI reporting performance by 40%.
• Integrated Athena for self-service querying and ad-hoc analytics, enabling business users to run queries without engineering support.
• Implemented AWS Lambda functions for real-time ingestion and automated triggers, reducing batch dependencies.
• Built CloudWatch dashboards and alarms for proactive monitoring of pipelines, improving SLA adherence by 20%.
• Designed data validation scripts in Python and SQL to enforce quality checks, ensuring 99% accuracy in reporting datasets.
• Optimized S3 storage with lifecycle policies, Parquet/ORC formats, and partitioning, reducing costs by 18%.
• Supported cross-functional teams by delivering datasets for data science, ML pipelines, and BI dashboards.
• Mentored junior engineers on AWS best practices, PySpark development, and SQL optimization.
• Participated in Agile ceremonies, sprint planning, and code reviews to ensure timely and high-quality deliveries.

Environment: Python, PySpark, SQL, AWS (S3, Glue, EMR, Redshift, Lambda, Athena, CloudWatch), Airflow, Git, Docker, Terraform, Agile/Scrum.

• Built Python automation scripts for ingestion from APIs, FTP, and databases, reducing manual effort by 40%.
• Migrated on-prem SQL Server data warehouse to AWS (S3 + Redshift), improving scalability and reducing query time by 35%.
• Developed ETL workflows to consolidate structured, semi-structured, and unstructured data sources into a unified data lake.
• Created and optimized SQL queries, stored procedures, and triggers for reporting and validation.
• Designed PySpark pipelines for batch processing and applied data cleansing, aggregation, and transformation rules.
• Used Glue crawlers to catalog data assets, improving discoverability and schema management.
• Optimized storage formats with columnar formats (Parquet/ORC), reducing query costs by 22%.
• Built CloudWatch metrics and alarms to monitor ETL job performance and detect anomalies.
• Partnered with BI teams to build Redshift-based analytics datasets for dashboards in Tableau and Power BI.
• Assisted in building CI/CD pipelines for automated deployments of ETL jobs into AWS environments.
• Documented data pipelines, workflows, and best practices to support onboarding and knowledge sharing.

Environment: Python, SQL, PySpark, AWS (S3, Glue, Redshift, CloudWatch), SQL Server, PostgreSQL, Airflow, Git, Jenkins, Agile/Scrum