Dec 2023 - Jun 2024
Alps Web Solutions
Software Developer
Built backend systems for an inventory management platform, focusing on search performance, transaction-safe checkout, dashboard analytics, caching, and reorder planning for small and regional stores.
Role Overview
A quick read on what the role was and the main areas I worked on before the deeper implementation details.
Built backend systems for an inventory management platform that handled product inventory, checkout, sales tracking, inventory history, dashboard reporting, and reorder recommendations for small and regional stores.
Optimized product search across 100,000+ records using composite indexes, filtered queries, and pagination, reducing average search latency from around 1.2 seconds to under 120 milliseconds.
Designed transactional checkout flows and concurrency-safe stock updates so sales, inventory changes, and history records stayed consistent during simultaneous purchases.
Improved dashboard performance by replacing repeated live aggregations with precomputed daily summaries and cached store-level metrics, cutting dashboard response time by around 80-90%.
Processed 500,000+ sales records into analytics and reorder recommendations using sales history, stock levels, and demand patterns to help stores make better restocking decisions.
Detailed Work
The role is grouped by major workstreams, with each one kept concise enough to explain the implementation and impact without turning into a wall of text.
Search Performance and Inventory Query Optimization
Optimized the product-search path for a large inventory dataset so store staff could filter and find products quickly without overloading the database as the catalog grew.
Optimized product search across 100,000+ records using composite indexes, filtered queries, and pagination so the API stopped scanning large portions of the inventory table on every request.
Narrowed queries by fields like store, category, status, and search text, then aligned composite indexes to those access patterns so common filters worked with the database instead of against it.
This reduced average search latency from about 1.2 seconds to under 120 milliseconds and made the backend more scalable as more stores, products, and users were added.
Transaction-Safe Checkout and Inventory Consistency
Built the checkout flow so sales, stock deductions, and inventory-history writes happened atomically and did not allow overselling during concurrent purchases.
Designed checkout as a transactional backend flow where sale creation, stock deduction, and inventory-history writes succeeded or failed together instead of leaving inventory in a partially updated state.
Used concurrency-safe stock updates to ensure inventory was reduced only when enough quantity existed at write time, which prevented negative stock during simultaneous purchases.
Maintained a stock-history ledger for sale, adjustment, return, restock, and damage events so the platform could explain why inventory changed instead of only showing the current number.
Dashboard Caching, Analytics, and Reorder Intelligence
Reworked the reporting path so dashboards and inventory insights could be served from summaries and caches instead of repeated heavy aggregations over raw sales data.
Replaced repeated live dashboard aggregations with precomputed daily sales summaries and cached store-level metrics, improving dashboard performance by around 80-90%.
Applied a cache-aside strategy for dashboard and analytics reads, with store-scoped cache keys and invalidation after important writes so performance improved without treating cache as the source of truth.
Processed 500,000+ sales records into analytics outputs such as top products, slow-moving inventory, category revenue, low-stock alerts, and reorder suggestions based on 30-90 days of sales history.