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Platform2026

Enterprise Property Management System

A comprehensive real estate platform for managing contracts, billing, and predictive analytics.

Live demoPrivate project

Developed a full-stack platform to automate complex real estate operations, replacing manual processes with an intuitive dashboard, contract lifecycle management, and dynamic financial reporting.

Technology stack

AngularTypeScriptRxJSPythonDjango REST FrameworkPostgreSQLDocker

Problem

The client needed a system capable of handling complex rental agreements, flexible growth schedules, and real-time utility calculations without compromising on loading speeds under heavy data loads.

Solution

Built a scalable Django REST Framework backend coupled with a highly responsive Angular frontend. Implemented custom RxJS state management to eliminate loading spinners and ensure a seamless UX.

Technical contribution

Led the architectural design and full-stack development, focusing on complex ORM queries, client-side caching, and building comparative analytics with future forecasting capabilities.

Architecture

Utilized a cleanly separated architecture: thin API views, dedicated selector and service layers in Django, and modular Angular components utilizing ChangeDetectionStrategy.OnPush.

Performance considerations

Dramatically improved API response times by resolving N+1 database query issues using prefetch_related and select_related, and shifted heavy mathematical computations directly to the PostgreSQL level.

Security considerations

Implemented strict role-based access control, secure JWT authentication, and protected API endpoints to ensure sensitive contract and financial data remains secure.

Deployment model

Containerized the application using Docker and successfully deployed it on a Contabo VPS, ensuring isolated and stable environments for the backend, database, and background workers.

Outcome

Delivered a high-performance, enterprise-ready platform that optimized contract processing time and provided stakeholders with instantaneous, data-driven financial projections.