RUSHIKESH GHATE

Key Contributions and Impact

• Designed and maintained production-grade data pipelines using Python, Py-Spark, AWS Glue, and S3, supporting batch processing, incremental loads, and BI consumption.
• Contributed to the cloud migration of on-prem ETL workflows to AWS, improving scalability and reducing end-to-end execution time from 10 hours to 4–5 hours.
• Optimized Py-Spark jobs through partitioning strategies, join optimization, memory tuning, and efficient file formats, significantly improving performance and cost efficiency.
• Implemented data validation, anomaly detection, and quality checks, improving data reliability and consistency for downstream analytics.
• Automated repetitive operational tasks using Python scripts, reducing 1-2 hours of daily manual effort, and minimizing human errors.
• Designed analytics-ready data models and supported BI teams by delivering clean, well-structured datasets for Power BI and Tableau dashboards.
• Collaborated with cross-functional teams (analytics, QA, and stakeholders) to ensure SLA-driven delivery and smooth production support.
• Created and maintained technical documentation covering pipeline architecture, workflows, and troubleshooting guidelines.

Key Achievements

• Reduced ETL runtime by 50–60% through Spark and workflow optimization.
• Improved data freshness and reporting turnaround for business stakeholders.
• Strengthened production stability through monitoring and validation frameworks.

• Performed data-driven structural analysis using ANSYS Workbench to evaluate design durability and compliance.
• Conducted pre-processing and post-processing of large engineering datasets, generating detailed analytical reports.
• Developed strong foundations in analytical thinking, data interpretation, and technical documentation, later applied to data engineering roles.