Step 1: Ideation & Technical Document

I take the business user inputs, structure them in a more technical way, and conduct a brainstorming session with an AI assistant (Gemini, Claude, or OpenAI). Once I am satisfied that all the missing pieces are covered and I have a clear picture of the APIs, database schema, and folder structure, I convert all of this into a comprehensive technical document.

Step 2: Boilerplate Code

Once the technical document is clear and unambiguous, I start by creating very basic boilerplate code that strictly adheres to the folder structure outlined in the technical document. This establishes the foundation.

Step 3: Pragmatic TDD

Per the requirements, I tried to stay as close as possible strictly to Test-Driven Development (Red → Green → Refactor). However, showing the practical use of TDD via granular commit history can be a tedious job (committing each individual failing test, working code, again testing, working, etc.).

To be pragmatic, I took a bit of leniency and committed grouped functional tests at once alongside the working code that satisfied them.

Step 4: Layered Step-by-Step Build

I also like to write down a step-by-step approach of what I want to build. As you can see in the commit history, I will first write very basic, minimally functional code without validation, overarching business rules, etc., and then slowly iteratively add up each layer to make the application feel complete and robust.

Step 5: Production-Ready Additions

I added a substantial amount of "extra" code like validations, user authentication, advanced filters (e.g., ilike matching), offsets for pagination, rate limiting, and global logging.

The primary reason for this is that a core requirement stated this code needs to be production-ready, which implies these foundational security and usability features are must-have basic needs the code should support out of the box.

Technical Reference

The following sections outline the precise technical architecture, system design, and API contracts used to build this backend application.

1. Project Overview

A high-performance, TDD-driven Python backend utilizing FastAPI, SQLAlchemy, and Alembic. The system manages employee data and calculates dynamic salary deductions based on country-specific tax policies stored in a versioned, data-driven format.

2. Test Credentials

The application endpoints are protected by Basic Authentication. Use the following default seeded credentials depending on the role you want to evaluate:

Role	Username	Password	Permissions
Admin	`admin`	`adminpass`	CREATE, READ, UPDATE, DELETE
Manager	`manager`	`managerpass`	CREATE, READ, UPDATE
Staff	`staff`	`staffpass`	READ (View Only)

3. Architecture Principles

Clean Architecture (Hybrid): Layered separation between API (Interface), Services (Business Logic), and Models (Persistence).
Active Record Pattern: Database models are "smart" and encapsulate their own CRUD logic (e.g., Employee.save(), Country.get_by_iso()).
Strict TDD: Development follows the Red → Green → Refactor cycle.
Data-Driven Policy: Tax logic is not hardcoded; it is retrieved from the countries table at runtime.
Statelessness: The API is designed to be horizontally scalable and Docker-ready.

We are utilizing a Hybrid Repository Pattern:

The Models: Act as "Smart Entities" containing self-contained CRUD logic.
The Repositories: Act as the orchestration layer used by Services to avoid raw SQL generation. Allows for easy mocking in TDD.

4. Technology Stack

Language: Python 3.10+
Framework: FastAPI (Asynchronous support)
ORM: SQLAlchemy 2.0+
Database: SQLite (Initial), PostgreSQL (Target Production)
Migrations: Alembic
Validation: Pydantic v2
Testing: Pytest (Unit & Integration)
Logging: Structured Logging

5. System Design & Data Models

Table	Column	Type	Constraints
Country	`iso_code`	String(2)	Primary Key, Unique
	`name`	String	Unique
	`current_deduction`	Float	Default TDS %
	`tax_slabs`	JSON	Meta-info for versioned slabs
Employee	`id`	UUID	Primary Key
	`full_name`	String	Required
	`salary`	Float	Precision managed in Service
	`country_iso`	String(2)	Foreign Key -> Country
UserAccess	`username`	String	Primary Key, Indexed
	`role_name`	String	Required
	`permissions`	JSON	Required

6. API Contract (v1)

All API endpoints return a unified response schema utilizing the StandardResponse model wrapper:

{
  "status": "success|error",
  "status_code": 200,
  "message": "Operation description",
  "data": { ... },
  "errors": null,
  "pagination": null
}

Core Endpoints

POST /api/v1/countries: Upsert Country.
GET /api/v1/countries/list: List all countries.
POST /api/v1/employees/create: Create new employee.
GET /api/v1/employees/list: List all employees.
GET /api/v1/employees/salary-breakdown/{id}: Returns Gross, Total Deductions (calculated via Country DB record), and Net Salary.
GET /api/v1/analytics/salary/by-country/{iso}: Min, Max, Avg salary.

7. Directory Structure

.
├── src/                         # Application Source
│   ├── api/                     # v1 routers and schemas
│   ├── core/                    # Config, logger, exceptions, middlewares
│   ├── database/                # Base model (CRUD logic), session
│   ├── models/                  # DB Entities
│   ├── repositories/            # Data Orchestration
│   ├── services/                # Math calculation and Analytics
│   ├── migrations/              # Alembic
│   ├── app.py                   # FastAPI Initialization
│   └── migration.py             # CLI Utility
├── tests/                       # Pytest Suite
└── Dockerfile                   # Multi-stage production build

Future Enhancement Roadmap

The following enhancements can be implemented to further optimize performance and scalability as the system grows to handle millions of records:

1. Caching Mechanism

Implement multi-layer caching (Redis/Memcached) for frequently accessed data:

Cache country tax configurations with TTL-based invalidation
Cache salary analytics results to avoid recalculation on repeated requests
Implement cache warming strategies for peak-access periods
Use ETag/HTTP caching headers for client-side optimization

Benefits: Reduced database load, significantly improved response times for analytics queries, better scalability under high concurrent load.

2. Pre-Computed Employee Counts with Database Triggers

Store aggregated employee counts per country with automatic synchronization via database triggers:

Add employee_count and total_salary_sum fields to the Country model
Create AFTER INSERT/DELETE/UPDATE triggers on the Employee table to maintain country totals automatically
Index these aggregated fields for O(1) lookups and fast sorting operations
Implement similar pre-computed sum and average fields for salary statistics by country

Why This Matters: When the employee count reaches millions, computing aggregations on-the-fly becomes prohibitively expensive. Pre-computed values with trigger-maintained accuracy enable constant-time lookups while ensuring data consistency.

3. Job Title-Based Analytics with Pre-Computation

Extend the trigger-based pre-computation pattern to job titles and departments:

Create a JobTitleAnalytics table with fields: job_title, employee_count, total_salary, avg_salary, min_salary, max_salary
Implement database triggers to update aggregates whenever employee records are created, updated, or deleted
Support salary range queries by job title with pre-computed min/max values
Enable cross-department comparison and cost center analysis without expensive joins
Store update timestamps for audit trails and change tracking

Why This Matters: Real-time analytics across multiple dimensions (country, job title, department) requires pre-computed aggregations at scale. This approach maintains data consistency while enabling sub-millisecond query responses.

Implementation Strategy

These enhancements follow pragmatic TDD practices:

Start with integration tests for the new features (triggers, caching logic)
Implement database migrations using Alembic with reversible scripts
Gradually refactor existing analytics endpoints to leverage pre-computed data
Add cache invalidation strategies to maintain consistency
Profile and benchmark against the previous implementation
Roll out feature flags to enable gradual adoption

Conclusion

I might have gone a bit low in complexity at some places and high at other places. But this is what I was able to securely architect and code in approximately 5 hours. I tried my best to stay as close as possible to the TDD system. If I had more time, there are certainly more enhancements I would wish to implement.

Specifically, this application is architected with scalability in mind. The three key enhancements outlined in the Future Enhancement Roadmap section are designed to handle growth to millions of employees without performance degradation:

Caching Mechanism: Dramatically reduce database load and response times for repeated queries
Pre-Computed Aggregates with Database Triggers: Enable O(1) lookups for country-level statistics at massive scale
Job Title Analytics: Real-time multi-dimensional analytics without expensive joins

Implementation Journey & Thought Process