Application Support Portfolio — Full‑Stack Banking Operations

This repository brings together four complementary projects that demonstrate the technical breadth, analytical mindset, and operational discipline expected from a senior Application Support professional in a banking environment.

It showcases end‑to‑end capabilities across ETL pipelines, operational tooling, backend diagnostics, and frontend support interfaces — the core pillars of modern support for risk and finance applications.

---

Repository Structure

/
├── banking_data_pipeline_python/     # Credit Risk ETL (Python)
├── it_ops_automation_toolkit/        # Shell + Python Ops Tools
├── support-portal-backend/           # Spring Boot Support API
└── support-portal-frontend/          # Angular Support Portal UI

Each module is self‑contained and can run independently.

---

Project Overview

1. Credit Risk ETL Pipeline (Python)

A production‑style ETL that computes Risk‑Weighted Assets (RWA) using standard credit‑risk parameters (EAD, PD, LGD).
Includes data‑quality checks, logging, error handling, and SQLite output.

Run:

pip install -e banking_data_pipeline_python
python -m banking_data_pipeline.rwa_calculator

---

2. IT Ops & Automation Toolkit

A set of lightweight tools used for daily operational checks and incident triage.

• health_check.sh — process, disk, and file monitoring
• log_analyzer.py — parses logs and summarizes WARN/ERROR/FATAL

These scripts reflect real support workflows: control points, monitoring, and rapid diagnostics.

---

3. Support Portal Backend (Spring Boot, Java 17)

A REST API exposing transaction data and surfacing failures with clear error codes.
Includes health checks, filtering endpoints, and an H2 in‑memory database for fast prototyping.

Run:

mvn clean spring-boot:run

---

4. Support Portal Frontend (Angular)

A simple UI for viewing system health and failed transactions.
Designed to give support teams quick visibility into issues.

Run:

ng serve

---

End‑to‑End Flow

The four modules together simulate a realistic support ecosystem:

1. ETL pipeline processes credit‑risk data.
2. Ops toolkit monitors the environment and analyzes logs.
3. Backend API exposes diagnostic information.
4. Frontend UI presents it clearly for support teams.

This mirrors the lifecycle of supporting a banking application:
data → monitoring → diagnostics → user visibility.

---

Skills Demonstrated

• Python (ETL, data quality, logging)
• SQL (SQLite, H2)
• Java (Spring Boot)
• Angular (TypeScript)
• UNIX Shell scripting
• Operational monitoring & log analysis
• Credit‑risk concepts (RWA, PD, LGD, EAD)
• Support‑oriented design and troubleshooting mindset

---

Purpose

This monorepo serves as a compact, practical demonstration of:

• Technical proficiency across the full support stack
• Ability to build tools that improve reliability and reduce MTTR
• Understanding of credit‑risk data flows
• Experience designing clear, support‑friendly diagnostics
• A structured, methodical approach to application support