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Hi๐Ÿ‘‹๐Ÿป, I'm Achraf OUJJIR

Cloud Data Engineer at Deloitte ๐ŸŸข| Builds Data Pipes for fun ๐Ÿ› ๏ธ | Passionate about Cloud โ˜๏ธ, Infrastructure ๐Ÿ—๏ธ and GenAI ๐Ÿง 


  • ๐Ÿ”ญ Iโ€™m currently working on publishing my projects on github ๐Ÿ™ƒ
  • ๐ŸŒฑ Iโ€™m learning Advanced Cloud Computing โ˜๏ธ and Data Platforms ๐Ÿ—๏ธ
  • โšก I have a thing for Hackathons (Gotta love the adrenaline rush ๐Ÿ”ฅ๐Ÿ‘จโ€๐Ÿ’ป)
  • ๐Ÿ“ซ How to reach me: [email protected] or Linkedin Badge

Whoami ๐Ÿ‘จโ€๐Ÿ’ป :

โ€œData is not just a field. It's a way of life.โ€


isagi-goal

I believe Graph Theory, Probabilities and Markov Chains are appliable in our daily life. We can't make the right decisions without the right data, and this is why it is important to have a data-centered thinking. This is the approach I try to embody in my career ๐Ÿ’ผ, studies ๐ŸŽ“ and health ๐Ÿ‹๏ธโ€โ™‚๏ธ.

As much as I enjoy leveraging data in my personal life, I get the same joy when doing it on a larger scale with big clients as a Cloud Data Consultant. I build and optimize solid data platforms in the cloud for data solutions to ensure seamless data workflows and integrations. My passion lies in optimizing data processes and enabling insightful analytics that support strategic decision-making and fuel business growth.

Metadata ๐Ÿ—‚๏ธ๐Ÿท๏ธ :

๐ŸฅŠ When I'm not exploring new tech, prepping for a cert or tackling leetcode problems, you'll find me boxing. It's a time for me when I let go of the steering wheel and allow my body to take control and move instinctively.

ippo-box

๐Ÿ’ช๐Ÿƒโ€โ™‚๏ธI also love to run and workout. Moving is a must for me. I usually get between 3 to 6 workouts and 2-3 hours of jogging per week. BDNF helps with leetcode ๐Ÿงฉ๐Ÿ‘จโ€๐Ÿ’ป.


izuku-run

๐Ÿš€๐Ÿ† I often team up with friends to take on hackathons. There's something amazing about collaborating with like-minded people, each bringing their own unique skills to create a well-rounded team ๐Ÿ‘ฅ. The thrill of quickly developing a prototype together is unmatched. If I had to use a football analogy โšฝ, it's like making a blind pass, knowing your teammate will be exactly where they need to beโ€”and they are! Having reliable people to work with is a true blessing ๐Ÿ™Œ, and the excitement of winning together makes it all the more rewarding ๐Ÿฅ‡!


isagi-dap

Languages and Tools ๐Ÿ› ๏ธ :

Cloud, DevOps & Infra โ˜๏ธ๐Ÿ—๏ธ:

aws azuredevops azure gcp terraform docker kubernetes github actions argocd git gitlab

Data Engineering & DBMS ๐Ÿ“Š๐Ÿ—ƒ๏ธ:

airflow azuresql cosmosdb dynamodb cassandra hadoop hive kafka spark mysql oracle postgresql mssql redis sqlite

Data Science and Machine Learning ๐Ÿค–๐Ÿง 

matplotlib pandas scikit_learn seaborn tensorflow pytorch

Development ๐ŸŒ:

python c csharp java dotnet fastapi html5 css3 nginx spring streamlit swagger django flask postman unity

Scripting & Automation โš™๏ธ๐Ÿ“œ:

bash powershell selenium

Operating Systems ๐Ÿ–ฅ๏ธ:

centos debian fedora linux ubuntu

Readme Card Readme Card Readme Card Readme Card


Github Stats

You can find me on

Achraf OUJJIR's Projects

chatgpt-users-tweets-pipeline icon chatgpt-users-tweets-pipeline

๐Ÿฆ๐Ÿ”ตEnd-to-end ChatGPT Users' Tweets Data Pipeline with Python ๐Ÿ, Hive ๐Ÿ, and Power BI ๐Ÿ“Š

khaimaai icon khaimaai

๐ŸŒ‹ Project for Morocco Solidarity Hackathon ๐Ÿค

musician-app icon musician-app

NodeJS / React sample app for AWS CI/CD pipeline tutorial

my-react-app icon my-react-app

This is a basic react app that I will deploy online using the AWS CI/CD stack (CodeCommit, CodeBuild, CodeDeploy and CodePipeline)

termgpt icon termgpt

๐Ÿค–๐Ÿง  Giving LLMs like GPT-4 the ability to plan and execute terminal commands ๐Ÿง๐Ÿ’ป

unity-martian-oxygen-quest-3d-game icon unity-martian-oxygen-quest-3d-game

๐Ÿช Unity Martian Oxygen Quest 3D Game ๐Ÿš€ Help Astro ๐Ÿ‘จโ€๐Ÿš€ collect oxygen tanks ๐Ÿ’ง! Built with Unity ๐ŸŽฎ

unity-solar-system-3d icon unity-solar-system-3d

๐Ÿช๐ŸŒUnity Solar System 3D ๐ŸŒŒ 3D solar system with dynamic planetary orbits ๐ŸŒ  using Unity ๐ŸŽฎ

xception-on-ham10k icon xception-on-ham10k

In this project, we used a transfer learning approach to build an image classification model for the classification of skin lesion, we trained our model specifically on the ham10000 dataset available on kaggle and we were able to achieve a 93.6% accuracy

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