Manoj Panchakarla

About

Backend-focused engineer with experience at Ramp, Easebuzz, and George Mason University, spanning data pipelines, workflow automation, and production reliability. Stands out for quantifiable performance gains, strong debugging instincts in distributed job systems, and translating ambiguous finance operations processes into measurable automation outcomes.

Key Strengths

• Built Python data pipelines for large experimental datasets
• Improved analytical workflow processing time by 30%
• Reduced execution time by 25% through batch optimization and eliminating redundant computation
• Designed structured preprocessing and aggregation flows for consistency and reproducibility
• Anticipates failure modes including data inconsistency, resource constraints, dependency changes, and processing errors
• Implements layered reliability practices with unit tests, integration tests, logging, and graceful error handling
• Turned an ambiguous manual expense approval process into an automated workflow
• Partnered directly with finance operations to clarify requirements and model exceptions
• Defined measurable automation success criteria including 50% reduction in manual processing time and 98% categorization accuracy
• Debugged flaky production automation using CloudWatch logs, RabbitMQ metrics, and Spring Boot service analysis
• Identified retry storms and peak-hour rate limit issues as root causes of failures
• Improved reliability by introducing exponential backoff, jitter, and queue prioritization
• Reduced failure rates by 25% and decreased duplicate processing incidents

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