À propos de Nassim
I'am a Tech Lead specialized in Big Data Engineering with more than 8 years of professional experience in this IT field, I was active in the energy, highway, health, tourism and banking sectors, and worked in international environments.
I have a solid knowledge of ML and AI, I work mainly on Cloud (AWS and MS Azure) and Big Data environments as Databricks using Spark with Python, my daily job consists of processing big data and extracting value from it.
Key Skills :
- Big Data Processing & Business Intelligence (Creating near real-time or batch processing data Pipelines, Data cleaning, Data Qualiy, Data Integration, Data Warehousing, SQL and NoSQL, Interactive Dashbords)
- Data science (Classification, Time-Series Forecasting, Causal Analysis, Graphical and Probabilistic Modeling of Industrial Problems, Data Vizualisation, NLP and semantic analysis)
- Applied mathematics (Optimization/Operational Research)
- Working in Cloud environments (AWS and AZURE) and Containers environment with Docker.
- I have been mainly deploying resources on AWS using CloudFormation YAML templates.
I usually accomplish Exploratory Data Analysis and Machine Learning tasks by using libraries like : Scikit-learn, Scipy, PySpark, Pandas, Numpy, TensorFlow, Seaborn ... etc.
- I'am passionate about Data Engineering, Scientific Programming and Quantitative Trading.
Français
Bilingue ou natif
Anglais
Bilingue ou natif
Arabe
Bilingue ou natif
Accepte de travailler sur site
Lyon 3e Arrondissement (jusqu’à 50 km), Lyon (jusqu’à 10 km), Paris (jusqu’à 100 km), Rennes (jusqu’à 100 km), Nantes (jusqu’à 100 km)
Expériences
- ARKEMAData Tech LeadCHIMIEoctobre 2022 - Aujourd'hui (3 ans et 8 mois)Lyon, FranceContext - Arkema HIP Datalake Project: Design and implementation of adatalake to manage the company's various domains (sales, authorizations,quality, environment-health-safety, master data, etc.) while also meetinginternal application needs.• Design and Prepare S3 buckets: Landing – Raw – Standardized –Published, on 3 environments: Dev, No-Prod and Prod.• Creation of a data glue catalog containing several databases for eachdomain of the company (sales, authorizations, quality, environment health-safety, master data etc.).• Implement Serverless ETL & ELT data flows with AWS lambda, Glue,Athena and StepFunctions, which are triggered by file drop events on theBucket Landing or on scheduled events, and develop data integration testsassociated with these flows.• Implement Near Real Time flows for on-the-fly data processing usingKinesis Firehose, Lambda and SQS services to create a Sales Datalake.• Verify the received files and ensure compliance with the Datalake spec,clean and aggregate the data according to different use cases.• Publishing data feeds to internal databases from the published layer of thedatalake (files publushing and/ or POST requests, ODBC connection withan RDS etc) and authorizing internal clients to consume data by scope(Power BI, IBM analytics, etc.)• Create the CloudFormation YAML templates that enable the creation ofAWS resources (Infrastructure as Code), and save them to a repository onGitLab which also manages the DevOps CI/ CD chain via automatedpipelines (stages: unit tests, code scan, linting, data quality, integrationtests, deploy)• T echnical monitoring via Datadog, and functional monitoring via aninternally developed web application using Next.JS with an AWS backend(Lambda, API GATEWAY, Dynamo DB)T echnical Environment:• AWS (S3, Lambda, StepFunctions, Kinesis, API Gateway, CloudFormation,Glue, Athena, Lakeformation, CloudWatch)• Python, TypeScript, ShellScript• GitLab, Datadog, Veracode
- ARaymondAWS Cloud Data EngineerINGÉNIERIE MÉCANIQUEoctobre 2021 - octobre 2022 (1 an)Grenoble, FranceContext• ARaymond Datalake Project: Design and implementation of a Data Centricplatform for the consolidation of industrial data.Achievements• ARAYMOND :• Design and Prepare S3 buckets: Landing – Raw – Curated – Normalized –Apps, on 3 environments: SandBox, Non-Prod and Prod.• Implement Serverless ETL flows with AWS lambda, Athena, Glue andStepFunctions, which are triggered by file drop events on the BucketLanding, and develop data integration tests associated with these flows.• Verify the received files and ensure compliance with the Datalake specs.• Clean and aggregate data according to use cases (Ex 1: ARIMS internalapplication for retrieving production data by Batch number or Marking. Ex2: ML Forecasting project using data from the datalake to predict thevalues of some sales business objects).• Indexing data on AWS OpenSearch (Serverless Elastic Search).• Exposing ARAYMOND industrial data via REST endpoints (AWS APIGATEWAY) for an internal application.• Create the CloudFormation YAML templates that enable the creation ofAWS resources (Infrastructure as Code), save them in an AWSCodeCommit Repository, and then deploy the resources via CodePipeline.
- MICHELINCloud Data EngineerSÉCURITÉ CIVILEmai 2021 - octobre 2021 (5 mois)Lyon, FranceContext• DDI (Drive Data to Intelligence): Using the potential of data from IoTdevices installed in vehicles for safer mobility.Achievements• Perform queries at the request of internal Michelin departments(Customer Success, Data Scientists DDI, CTO) on the Data Lake containingdata from devices (IoTs) installed on vehicles traveling in France and theUnited States.• Perform the various ETL operations via DataBricks (PySpark) andDataFactory for DDI Data Scientists and external clients (ensure dataavailability).• Automate export flows to client environments (FTP and AWS S3) byscheduling DataFactory Pipelines to run recurrently.• Perform data integration tests (Move from MongoDB to Azure) onpipelines calling DataBricks notebooks.• Present aggregated data on dashboards (Power BI to show to clients andRShiny for exploratory analysis internally for Michelin partners).T echnical Environment:• MS Azure (ADLS GEN2, Data Factory, Devops), DataBricks (Python -Pyspark), AWS (S3, Athena), Power BI, RShiny, GitLab, JIRA.Functional Environment:• Big Data Environment• Scrum methodology• Pneumatics industry sector
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Formations
- Diplôme d'Ingénieur en Informatique et LogistiqueEcole Nationale Supérieure d'Informatique et d'Analyse des Systèmes (ENSIAS)2018M1.1 Algorithmique & Programmation 56 H M1.2 Structures de données 60 H M1.3 Electronique numérique 57 H M1.4 Architecture des ordinateurs 56 H M1.5 Eléments de Recherche opérationnelle 59 H M1.6 Probabilité Appliquées 52 H M1.7 Gestion, Economie et Finance 1 58 H M1.8 Langue et communication 1 52 H Semestre 2 (438 H) : Tronc commun Code du module Intitulé du module VH global du module M2.1 Bases de données Relationnelles 59 H M2.2 Informatique théorique 57 H M2.3 Réseaux de communication 60 H M2.4 Système d’exploitation 52 H M2.5 Programmation Orientée Objet 48 H M2.6 Projet de filière 51 H M2.7 Gestion, Economie et Finance 2 59 H M2.8 Langue et communication 2 52 H Semestre 3 (442 H) Code du module Intitulé du module VH global du module M3.1 Système d’information 69 H M3.2 Management industriel et logistique 50 H M3.3 Génie Logiciel Objet 58 H M3.4 Méthodes Numériques Avancées 48 H M3.5 Statistiques et Analyse de Données 58 H M3.6 Techniques d’optimisation 60 H M3.7 Culture Entrepreneuriale 51 H M3.8 Langues et communication 3 52 H Semestre 4 (414 H) Code du module Intitulé du module VH global du module M4.1 Chaîne logistique stochastique 56 H M4.2 Modélisation de la chaîne logistique 48 H M4.3 Système d’information logistique 1 52 H M4.4 Techniques avancées d’optimisation 52 H M4.5 Réseaux logistiques et entreposage 50 H M4.6 Projet de fin d’année 48 H M4.7 Droit et Management 56 H M4.8 Langue et communication 4 52 H Semestre 5 (423 H) Code du module Intitulé du module VH global du module M5.1 Atelier de Modélisation 52 H M5.2 Projet fédérateur 52 H M5.3 Simulation de la chaîne logistique 48 H M5.4 Système d’information logistique 2 52 H M5.5 Systèmes logistiques prédictifs 56 H M5.6 E-Logistique 59 H M5.7 Amélioration des performances de la chaîne logistique 56 H M5.8 Anglais et stage de 2A
- Master 2 : Data ScienceUniversité Lyon 1 claude bernard2019DS1 - Graphes, Complexité, Combinatoire, 3 ECTS DS2 - Data Visualization, 3 ECTS DS3 – Big Data Analytics, 3 ECTS DS4 - Cloud Computing, 3 ECTS DS5 - Statistique Inférentielle, 3 ECTS DS6 - Modèles de Régression, 3 ECTS DS7 - Modèles Graphiques Probabilistes, 3 ECTS DS8 - Data Mining, 3 ECTS DS9 - Machine Learning, 3 ECTS DS10 - Fondamentaux Mathématiques pour les Data Science, 3 ECTS
Certifications
- Data Analytics - Mining and Analysis of Big DataAlison2018
- Python for Applied Data ScienceIBM & Coursera2018