Souhib Trabelsi

The mission: Development and deployment of a dashboard to be used by Snaplab team members to record all user sessions during tests of Snap's Connected Spectacles. Focus Area: ➤ Setting up the Backend project architecture (Python, FastAPI) ➤ Creation of the controllers allowing the management of users, scenes, sessions and analysis (Python, PostgreSQL, SQLModel) ➤ Creation of routes with input validation (Python, FastAPI, Pydantic). ➤ Implementation of unit tests and functional tests (Pytest). ➤ Created a Dockerfile to deploy the FastAPI API with Python on AWS ECS (Docker, ECS, ECR, Terraform). ➤ Deploying Python scripts on AWS Lambda (Docker, AWS Lambda, AWS ECR). ➤ Creating a database via the AWS RDS service (AWS RDS, PostgreSQL). ➤ Implementation of database migration (Alembic). ➤ Wrap the infrastructure inside a VPC that can be accessed through a Load Balancer (AWS VPC, AWS ALB) ➤ Deployment of React Typescript website on a domain name (AWS Route 53, AWS Amplify). ➤ Creation of a frontend dashboard (React JS, Typescript). ➤ Deploy the frontend dashboard on AWS Amplify (AWS Amplify, Terraform). Skills: ➤ Backend: Python (FastAPI) ➤ Database: ORM (SQLModel), PGSQL ➤ Cloud: AWS ➤ DevOps: Docker, Terraform, Ansible ➤ Testing: Pytest ➤ Versioning: Git

⇨ 𝐌𝐘 𝐌𝐈𝐒𝐒𝐈𝐎𝐍: Development and deployment of a platform allowing users who submitted a request to Enedis to follow up on their request and to be able to exchange with Enedis via a chat if necessary Focus Area : ➤ Setting up the Backend project architecture (Python, FastAPI) ➤ Creation of the controllers allowing the management of tickets, messages and users (Python, PostgreSQL, SQLModel) ➤ Creation of a controller providing KPIs related to user actions (Python, PostgreSQL, SQLModel). ➤ Creation of routes with input validation (Python, FastAPI, Pydantic). ➤ Implementation of unit tests and functional tests (Pytest). ➤ Created a Dockerfile to deploy the FastAPI API with Python 3.10 on AWS Lambda (Docker, AWS Lambda). ➤ Deploying the docker image on AWS ECR (Docker, AWS ECR). ➤ Use of serverless framework enabling automation of FastAPI API deployment (Serverless, AWS Lambda). ➤ Creating a database via the AWS RDS service (AWS RDS). ➤ Implementation of database migration (Alembic). ➤ Deployment of React Typescript website on a domain name (AWS Route 53, AWS Amplify). ➤ Setting up a CI / CD on Github Action (Git, Github Action). 𝐁𝐚𝐜𝐤𝐄𝐧𝐝: Python, FastAPI 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: ORM (SQLModel), PGSQL 𝐂𝐥𝐨𝐮𝐝: AWS 𝐃𝐞𝐯𝐎𝐩𝐬: Github Action, Docker, Serverless framework ,Terraform 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 : Pytest 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠 : Git

I participated in the creation, development, optimization, and deployment of APIs written in Python, allowing data scientist users to run IDEs connected to a NAS allowing them to access large volumes of sensitive data inside protected infrastructures Focus Area: ➤ Transitioning from a monolithic architecture to a microservice architecture (API, PostgreSQL) ➤ Setting up the structure of the new APIs (Python, FastAPI) ➤ Creation of controllers allowing the management of users and their access to the application and NAS (Python, PostgreSQL, SQLModel) ➤ Creation of a controller to create KPIs related to user actions (Python, PostgreSQL, SQLModel). ➤ Creation of routes with input validation (Python, FastAPI, Pydantic). ➤ Implementation of unit tests and functional tests (Pytest). ➤ Creation of a Dockerfile to deploy the FastAPI API on On-Promise servers. ➤ Deployment of docker images via Ansible (Docker, Ansible) ➤ Implementation of the database migration (Alembic). ➤ Creation of a pre-commit hook allowing the integration of a Local CI (Python, Pre-commit) ➤ Development of a notification banner on the user interface (React JS) ➤ Fixing issues reported by users on the frontend (React JS) ➤ Modification of CI / CD on Gitlab CI (Git, Gitlab). 𝐅𝐫𝐨𝐧𝐭𝐄𝐧𝐝: HTML, CSS, React JS, Styled Components 𝐁𝐚𝐜𝐤𝐄𝐧𝐝: Python, FastAPI 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: ORM (SQLModel), PGSQL, Alembic 𝐃𝐞𝐯𝐎𝐩𝐬: Gitlab CI, Docker, Ansible 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 : Pytest 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠 : GIT

⇨ 𝐌𝐘 𝐌𝐈𝐒𝐒𝐈𝐎𝐍: Develop solutions that enable companies to reduce the financial costs of their Cloud infrastructures. Focus Area: ➤ Retrieving compute instance prices from AWS and GCP (Python, AWS, GCP). ➤ Development of a Python script deployed on lambda allowing the update of compute instances prices in a database (Python, PGSQL, AWS, GCP). ➤ Development of a Python script deployed on Lambda allowing to turn on and off EC2 instances received in parameter. ➤ Development of a Python script deployed on a Google Cloud Function allowing to schedule the switch on and switch off of compute engines. ➤ Deployed a Gateway API and a lambda inside a VPC via Terraform. ➤ Deployment of a Lambda function triggered by a CloudWatch Event via Terraform. ➤ Deployment of a google scheduler and a google function via Terraform. ➤ Configuring a Gitlab Webhook to make an API call to an AWS API Gateway during a push event. ➤ Implementation of Back-End unit tests (Pytest). ➤ 𝐁𝐚𝐜𝐤𝐄𝐧𝐝: Python ➤ 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: MySQL, PGSQL ➤ 𝐂𝐥𝐨𝐮𝐝: AWS, GCP ➤ 𝐃𝐞𝐯𝐨𝐩𝐬: Terraform, Gitlab, Docker ➤ 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Pytest ➤ 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠 : Git