A service that supports simple CRUD operations for an in-house CRM system, maintaining data about existing customers and sales opportunities (leads).

• The service is not responsible for the authentication or authorization of the user. Assuming an authentication service is responsible for authenticating the user or implementation should follow.
• The server can have multiple load-balancer instances
• The service has information about existing employees and their roles in order to assign them to different customers / sales opportunities.

Using a FastAPI framework, the service exposes a REST API that allows the user to create/read/update/delete new customers or sales leads, by calling the appropriate endpoints. If a lead's status is updated to be 'Won' -> a new customer is automatically created in the database, having direct relationship with the lead.

• The service maintains a PostgreSQL database to keep track of the leads and customers.
• The database is set up using Alembic, which is a lightweight database migration tool for usage with the SQLAlchemy database toolkit for Python.

The service exposes a REST API that allows the user to create/read/update/delete new customers or sales leads, by calling the appropriate endpoints. The API is documented using Swagger UI, which is a tool that visually presents the API documentation. You can access the Swagger UI by opening http://localhost:8000/docs in your browser after running the server.

The solution is based on the following technologies:

• Python
• FastAPI
• PostgreSQL
• Pytest
• SQL Alchemy
• Alembic
• Pydantic
• Docker
• Docker Compose
• Swagger UI

• docker

• cd into the project directory
• Run docker-compose up --build
• Open http://localhost:8000/docs to see the API documentation and run the endpoint using the Swagger UI
  • Click on [Try it out]
  • Then [Execute] to run some endpoints with some data if needed.

• cd into the project directory
• Run make check to run the tests and static code analysis [The current test coverage is 95%]

This isn't a complete solution. If I had more time, I would have done the following:

• Store Employees data in a new database table and add a relationship between the leads/customers and the employees with a verification that the employee exists in the database before assigning it to a lead/customer
• Add more tests
• Add verification of duplicated entries in the database using a set of unique fields that could identify a lead/customer
• Add more user-input-validations. For example, the email field is verified but some other fields are not.
• Add authentication and authorization
• Add more tests to full coverage. Currently, 95% is covered.
• Deploy the service to a cloud provider
• Add more logging
• Add CI/CD
• Add more error handling
• Add more documentation

It's also possible to run the service locally without Docker, by following these instructions:

1. Install PostgreSQL: brew install postgresql
2. Create a db called "qonto": createdb qonto
3. Set up the environment variables in a new .env file (follow the .env.example file):
4. Create a virtual environment: python -m venv venv
5. Activate the virtual environment: source venv/bin/activate
6. Install the dependencies: make requirements start

Thanks for the opportunity, and I'm looking forward to hearing your feedback!