Charaf Eddine Chelloug

Intervention sur plusieurs projets data au sein de SUEZ, autour de la qualité, la consolidation et la valorisation des données RH et référentielles. ▸ Projet principal : référentiel tactique intégré au programme MyID - Animation d’ateliers avec les équipes métier, RH, IT et Data afin de cadrer les besoins, analyser les sources existantes et définir les règles de gestion. - Analyse et mapping de données issues de sources hétérogènes : fichiers plats, systèmes RH / paie, Active Directory et référentiels internes. - Contribution à la mise en place d’un référentiel collaborateur unique, fiabilisé et actualisé, permettant de consolider les informations les plus récentes par collaborateur. - Normalisation et standardisation des données RH afin d’améliorer leur qualité, leur cohérence et leur exploitabilité. - Collaboration avec l’équipe Data Engineering pour l’industrialisation des flux d’alimentation dans GCP. - Analyse, contrôle et restitution des données via Python, pandas, SQL/BigQuery et Power BI. ▸ Projet Recette Database RH - Conception et développement de traitements de contrôle pour la recette des données migrées vers GCP. - Comparaison automatisée entre l’ancienne base RH et la nouvelle plateforme : structure, contenu, écarts et qualité des données. - Mise en place d’outputs de contrôle avec score de conformité, détection des anomalies et export des écarts pour correction. ▸ Projet DFF Data Facturation - Reprise et déploiement d’un traitement utilisé par plusieurs applications et équipes SUEZ. - Mise en place de l’environnement sur machine virtuelle Azure avec Docker, Elasticsearch, Kibana, PostgreSQL et API Python/Django. - Contribution aux contrôles de rapprochement de données de facturation : SIRET, SIREN, TVA, scoring et détection d’anomalies. Environnement technique : Python, pandas, SQL, BigQuery, Power BI, GCP, Active Directory, fichiers plats, Docker, Elasticsearch, Kibana, PostgreSQL, Python/Django, Azure VM.

Projects As part of the digital transformation at SNCF Réseau, I contributed to the design and implementation of a Data Lake on AWS to centralize and analyze user data, thereby enhancing the user experience. Main Projects Datalake DLCU: ● Implementation of the Data Lake on AWS: Designing and deploying a scalable architecture to centralize user data. ● Automation of data processes: Setting up pipelines for automated data ingestion, processing, and storage. ● Integration of external data sources: Establishing secure connections with third-party APIs to enrich the Data Lake. Tasks ● Designing the Data Lake architecture in collaboration with the internal architect. ● Deploying AWS services such as Amazon S3, AWS Glue, Redshift, Lambda, and CloudWatch. ● Managing data quality: Implementing mechanisms to handle rejected data and ensure data integrity. ● Automating data processing pipelines: Orchestrating data flows across the Landing, Bronze, and Silver layers. ● Creating detailed technical documentation of the architecture and processes to facilitate maintenance and future development. ● Optimizing Data Lake performance by identifying and resolving bottlenecks. ● Collaborating with business teams, data analysts, and data scientists to meet analytical needs. Environment: AWS Services (S3, Glue, Redshift, Lambda, CloudWatch, SNS, SES, Lambda), Python, PySpark, SQL, Power BI, SharePoint Key Points: Implementing an AWS Data Lake to centralize and analyze user data, enabling business teams to enhance user experience and make data-driven decisions.

Project: As part of the industrialization of France 2030 reporting and the financial monitoring of the state’s investment plan, I contributed to the creation of a dataset that powers Qlik dashboards for two use cases: ● Collecting data from various sources, including PXL (MongoDB), FR30 Reporting, configuration tables (Excel hosted on SharePoint), as well as data from CLI (Comptoir Client) and CBS (Core Banking System), both hosted on AWS Athena. ● Storing and historicizing data in the S3 RAW bucket ● Cleaning and refining data, converting it to Parquet format ● Reconciling and processing data ● Filtering and exposing tables in the AWS Athena data catalog ● Performing unit tests with Pytest and Unit-test Technical Environment: AWS (Glue, S3, CloudWatch, Athena, SecretManager), PySpark3, Python3, GitLab, Jenkins, SonarQube, Terraform, Datadog, MongoDB, Jira, Neuronne, DataLink Methodology: Agile SAFe

Mission As part of a digital transformation process for an event management application, I worked on a video processing project. I also contributed to a finance use case regarding supplier payment delays: ● Development of a functionality enabling the automatic chaptering of a presentation, using Multi-Scale and Optical Flow techniques with Python and AWS ● Specification of the workflow for the finance use case ● Design and industrialization of the data preparation with Dataiku ● Creation of two dashboards with Tableau Technical Environment: AWS (Glue, EMR, S3), Docker, PySpark, Python3, Spark, OpenCV, GitHub, Mobaxterm, SAP, Dataiku, Tableau, Jenkins, Airflow Methodology: Agile Scrum

Mission As part of the deployment of the SmartCare solution provided by Huawei Telecom to the operator ATM Mobilis for service quality monitoring and management, I worked as a Data Analyst and Data Engineer: ● Administration and supervision of database platforms: Hadoop, Sybase IQ, PostgreSQL, and SQL Server ● Analysis and presentation of customer/marketing data with Dataiku ● Data preparation with Dataiku and Python ● Creation of dashboards with Tableau: Users and Device Insight, User Migration, and Segment Technical Environment: Hadoop, Python, Dataiku, SQL Server, Impala, ADS, DBeaver, Linux Shell, Tableau Software Methodology: V-Model

Mission As part of a digitalization program, I worked as a full-stack developer to implement software designed to manage the entire store supply chain: ● Managing product specifications: purchase and selling prices, quantities, images, expiration dates, multiple barcodes ● Managing entities: stock status and movements, invoices, profits, expenses, cash registers ● Analyzing and presenting data: daily, monthly, and annual reports; alarms and notifications ● Developing Python scripts for maintenance, reset, inventory, and data backup (automatic/manual, to removable disks/cloud), and data restoration at any time ● Managing user roles and restrictions (Admin, Moderator, Seller, and custom) ● Creating dashboards with Tableau to track products, purchases, sales, profits, and stock Technical Environment: C#, DevExpress, XtraReport, SQL Server, Tableau Software, Python

● TIMA Lab: https://tima.univ-grenoble-alpes.fr/ ● Implementation and validation of the algorithm for voice activity detection on the STM32F7 microcontroller Technical Environment: C / C++ (IAR Workbench)

As part of fundamental research on voice activity detection in a communication channel, I worked on developing a new algorithm that outperforms standard VAD methods: ● Development of a VAD algorithm based on binary statistical decision techniques (CFAR) using adaptive thresholding with MATLAB and C++. ● Development of VAD algorithms using Machine Learning techniques (K-means, SVM, and RFC) with Python (Scikit-Learn). ● Comparison and validation of the results against standard VAD methods, using the international NOIZEUS database. ● Implementation of the proposed techniques on an MCU STM32F7 board to validate real-time functionality. ● Publication of a research article in the IJEEI journal (Scopus). ● Presentation of research findings in international conferences. Technical Environment: C++ (IAR Workbench), Python (Scikit-Learn), MATLAB, Machine Learning Methodology: CFAR, MCU STM32F7