Guna Priya Sadhu

Develop full-stack features for Walmart’s e-commerce order management platform using Python (FastAPI/Django) and React, building services that handle millions of daily order and inventory updates across online and store fulfillment systems. Design and maintain RESTful APIs for order creation, inventory synchronization, and shipment tracking, supporting p95 response latency around 120–150 ms under peak traffic conditions. Build event-driven backend services using Kafka and Python workers to process 10M+ daily order and fulfillment events, enabling near real-time updates across warehouse and customer tracking systems. Optimize database performance across PostgreSQL and Redis, implementing indexing strategies and caching layers that improved order lookup and inventory query performance by 30%. Develop responsive front-end components using React, TypeScript, and Redux, improving customer and internal dashboard usability for order tracking and fulfillment operations. Deploy microservice

Design and develop Python-based backend services and REST APIs supporting real-time inventory visibility across stores, fulfillment centers, and e-commerce channels. Architect and build scalable, event-driven microservices to ingest high-volume inventory signals with strong consistency and availability requirements. Implement asynchronous pipelines for receipts, transfers, and fulfillment updates, reducing inventory update latency. Deliver full-stack features using Python services and modern frontend frameworks to support internal operations. Optimize backend performance using async processing, caching, and database query tuning, improving p95 API response times by peak traffic. Build data validation, reconciliation, and anomaly detection workflows to reduce downstream inventory mismatches. Integrate services with AWS infrastructure and CI/CD pipelines, improving deployment reliability and reducing release-related incidents. Implement structured logging, metrics, and alerting, reducing

Developed backend services in Python (Flask/FastAPI) supporting Adobe Experience Cloud personalization and analytics workflows, building APIs used to process millions of daily customer interaction events across enterprise marketing platforms. Built data processing pipelines using Python, SQL, and Apache Spark to transform and aggregate 1–2 TB of behavioral data, enabling real-time audience segmentation and campaign targeting for enterprise clients. Designed and maintained RESTful APIs for customer profile updates, audience activation, and campaign data ingestion, keeping p95 response latency around 140 ms for high-volume marketing workloads. Implemented feature engineering and data transformation scripts using Pandas and NumPy, improving data preparation efficiency for analytics and personalization pipelines by 30%. Optimized database performance across PostgreSQL and Redis, introducing indexing strategies and caching layers that reduced query response times for customer profile look