Application Support Portfolio — Full‑Stack Banking Operations

This repository contains four independent projects that reflect the core responsibilities of Application Support in banking: data processing, operational diagnostics, backend visibility, and user‑facing support tools.
Each module is self‑contained and demonstrates a specific capability used in daily support work.

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Repository Structure

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├── 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

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Project Overview

1. Credit Risk ETL Pipeline (Python)

A modular ETL that computes Risk‑Weighted Assets (RWA) using standard credit‑risk inputs (EAD, PD, LGD).
Includes data‑quality checks, structured logging, error handling, and SQLite output for traceability.

Run:

pip install -e banking_data_pipeline_python
python -m banking_data_pipeline.rwa_calculator

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2. IT Ops & Automation Toolkit

A collection of operational tools used for routine checks and incident triage.

• health_check.sh — process, disk, and file monitoring
• log_analyzer.py — log parsing with WARN/ERROR/FATAL extraction

These scripts model real support workflows: control points, environment checks, and fast diagnostics.

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

A REST API that exposes transaction data and failure causes for support teams.
Provides health checks, filtering endpoints, and an H2 in‑memory database for local analysis.

Run:

mvn clean spring-boot:run

Test Suite

The backend includes a MockMvc + Mockito test suite for API‑contract validation.
Tests use @WebMvcTest to isolate the web layer:

• Controllers and HTTP mappings only
• Repository and service layers mocked
• No database or full Spring context

This enforces Separation of Concerns and ensures stable, predictable responses for support workflows.

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4. Support Portal Frontend (Angular)

A simple UI that displays system health and failed transactions.
Designed to give support teams a clear view of backend status and failure patterns.

Run:

ng serve

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End‑to‑End Flow

The four modules form a compact support ecosystem:

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

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

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Skills Demonstrated

• Python (ETL, validation, logging)
• SQL (SQLite, H2)
• Java (Spring Boot, REST APIs)
• Angular (TypeScript)
• Shell scripting
• Operational diagnostics and log analysis
• Credit‑risk fundamentals (RWA, PD, LGD, EAD)
• Support‑oriented API and tooling design

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Purpose

This monorepo demonstrates:

• Breadth across backend, frontend, ETL, and operational tooling
• Ability to design systems that reduce MTTR and improve visibility
• Clear diagnostic surfaces for support teams
• Structured, maintainable engineering aligned with support requirements