Large-Scale Async Processing & Automation

High-Volume Data Export System

Enterprise clients frequently required massive dataset exports. The legacy system crashed under the weight of these requests and incurred massive cloud egress costs due to direct database access. I architected a streaming data export system utilizing chunked processing and memory-efficient Node.js pipelines. This eliminated direct database egress by routing data through optimized application-layer exports, reliably processing and downloading 6M+ records without crashes and reducing infrastructure costs by 40%.

POC Processing Automation

Proof-of-Concept (POC) testing previously involved manually executing API calls with complex payloads across Excel-based datasets — consuming up to 600 hours per cycle and severely lacking validation or progress tracking.

• Worker Architecture: Built a robust asynchronous pipeline using BullMQ and Redis.
• Dynamic Integration: Supported dynamic Excel parsing, payload templates, and live authentication logic.
• Fault Tolerance: Implemented dynamic concurrency scaling, live data validation, and fault-tolerant retry mechanisms for failed jobs.

Business Impact

The automated pipeline reduced manual processing effort from 600 hours down to just 12 hours — a 98% reduction. Individual transaction processing times dropped from 60 seconds to under 100ms, completely transforming the efficiency of the QA and integration teams.