Finance Operations•Full Stack Web Application•2024

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)

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. We built a centralized, production-grade web platform enabling secure Excel ingestion, automated financial operations, relational data storage, and role-based access for analysts, accountants, and managers.

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.

Key Features

✓Drag-and-drop Excel ingestion with real-time validation and error reporting

✓Custom financial calculation engine for batch updates, interest, and rebalancing

✓Role-based access control (RBAC) for analysts, managers, and admins

✓Historical data archiving with full audit trail and version history

✓Asynchronous background processing (Celery/Redis) for large file uploads

✓Interactive data visualization dashboards for portfolio performance

✓Pydantic validation layer with detailed 'dirty data' error reports

✓Docker containerized deployment with SSL and database encryption at rest

Challenges & Solutions

Implemented asynchronous background processing using Celery and Redis, allowing users to continue working while files process in the background — eliminating all timeouts.

Built a Pydantic + Pandas validation layer that audits file structure before storage and returns a detailed, human-readable error report for any 'dirty' data.

Engineered a Python calculation engine using the Decimal module with full unit test coverage, ensuring every interest and reconciliation calculation matched legacy audit requirements.

Containerized with Docker, enforced SSL, stored all secrets via environment variables, and implemented PostgreSQL encryption at rest — meeting financial-grade security standards.

Results

Manual Data Entry

40+ h/week→8 h/week

75% reduction

Version Conflict Data Loss

recurring→eliminated

centralized source

Calculation Error Rate

~3%→0%

precision engine

File Processing Capacity

5k rows→50k+ rows

async processing

Data Accessibility

siloed→centralized

searchable platform

Audit Readiness

4-5 hours→instant

full trail

Goals

•Transition firm from spreadsheet sprawl to a centralized SaaS platform
•Automate financial operations with 100% calculation accuracy
•Implement role-based access for data governance
•Ensure enterprise-grade security for sensitive financial data

Tech Stack

•Python
•FastAPI
•ReactJS
•PostgreSQL
•Pandas
•Docker
•AWS

Target Users

•Financial analysts
•Accountants and reconciliation teams
•Asset and portfolio managers

Key Learnings

•The file upload UI is as critical as backend processing — users need immediate feedback on data quality
•Async processing is non-negotiable for large financial datasets in a browser context
•Pydantic validation with descriptive error messages dramatically reduces support requests
•Financial math requires the Decimal module — floating point errors are unacceptable in production

Future Plans

•Add AI-driven predictive analytics module for trend forecasting
•Build automated regulatory reporting (PDF generation)
•Implement multi-currency support and FX normalization
•Add real-time data connections to market data feeds