Enterprise Financial Data Management & Automated Processing Engine
A production-grade SaaS platform replacing fragmented Excel workflows with centralized data ingestion, automated financial calculations, role-based access control, and audit-ready reporting, cutting manual effort by 75%.
- Client
- Investment Operations Provider (NDA)
- Industry
- Finance Operations
- Type
- Full Stack Web Application
- Year
- 2024
- Our role
- Full-Stack Backend & Database Architect
- Timeline
- 10 weeks
- Team
- 2 dev
Overview
The client managed critical financial data across dozens of fragmented spreadsheets, a workflow prone to version conflicts, data loss, and calculation errors.
Process.
- Designed a robust PostgreSQL schema for high-relational financial records.
- Built a FastAPI ingestion pipeline with real-time validation using Pydantic.
- Developed custom Pandas/NumPy modules for financial transformations.
- Created a ReactJS dashboard for file management, data visualization, and user administration.
- Deployed via Docker on AWS with full encryption.
What we built.
- featDrag-and-drop Excel ingestion with real-time validation and error reporting
- featCustom financial calculation engine for batch updates, interest, and rebalancing
- featRole-based access control (RBAC) for analysts, managers, and admins
- featHistorical data archiving with full audit trail and version history
- featAsynchronous background processing (Celery/Redis) for large file uploads
- featInteractive data visualization dashboards for portfolio performance
Hard parts.
fixLarge Excel files (50k+ rows) timing out during browser upload
Implemented asynchronous background processing using Celery and Redis, allowing users to continue working while files process in the background, eliminating all timeouts.
fixInconsistent formatting in user-uploaded Excel files
Built a Pydantic + Pandas validation layer that audits file structure before storage and returns a detailed, human-readable error report for any 'dirty' data.
fixFinancial calculations requiring 100% decimal precision
Engineered a Python calculation engine using the Decimal module with full unit test coverage, ensuring every interest and reconciliation calculation matched legacy audit requirements.
fixSecure deployment for sensitive financial records
Containerized with Docker, enforced SSL, stored all secrets via environment variables, and implemented PostgreSQL encryption at rest, meeting financial-grade security standards.
Built with
Python FastAPI ReactJS PostgreSQL Pandas Docker AWS
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