Jonathan Kini

Implementation of a datalake in R&D on data experimentation of laboratories for Servier biologists, chemists, pharmacologists. REALIZATIONS Complexity: a large volume of data (tens of TB) from screening machines and very complex business rules Implementation of batch data ingestion workflow Implementation of workflows for data cleansing and exposure TECHNICAL ENVIRONMENT GCP, Bigquery, Cloud Storage, DataFlow, Python, Argo, Airflow, Kubernetes, Pyspark, Git, Terraform

PROJECT SOLOCAL - Data Platform Implementation of a data platform that integrates sales and vendor data and makes it usable by the various Finance and Compensation squads REALIZATIONS ● Complexity: Many data sources to integrate and various data types (batch, streaming) ● Implementation of batch data ingestion workflow ● Implementation of streaming data ingestion workflow ● Implementation of workflows for data cleansing and exposure in the Data Platform TECHNICAL ENVIRONMENT Bigquery, Cloud Storage, Composer, Airflow, Python, Cloud Function, Pub/Sub, Git PROJECT LES ECHOS - LE PARISIEN - Customer Data Platform Implementation of a customer data platform that integrates data from all Les Echos - Le Parisien brands and makes them usable by the various marketing campaign systems REALIZATIONS ● Complexity: Many data sources to integrate and various data types (batch, streaming) ● Implementation of batch data ingestion workflow ● Implementation of streaming data ingestion workflow ● Implementation of workflows for data cleansing and exposure in the Customer Data Platform TECHNICAL ENVIRONMENT Bigquery, Cloud Storage, Composer, Airflow, Python, Cloud Run, Cloud Build, Git

PROJET - Caisse des dépôts & Consignation Implémentation d’un Datalake RÉALISATIONS Implémentation de workflow d'ingestion pour les fichiers plats (CSV, XML) Implémentation de workflow d'ingestion pour les bases de données Implémentation de jobs spark pour le nettoyage et traitement des données ingérées Implémentation de jobs spark pour le l'exposition des données pour des utilisations metiers (Tableau , data science ...) ENVIRONNEMENT TECHNIQUE Hortonworks, Hadoop, Spark, Hive, Kafka, PostgreSQL, Python, Git, Jupyter, Linux, Kerberos, Scrum PROJET - LA POSTE Détection de fraude RÉALISATIONS Définition d’une nouvelle architecture Migration de données de l’ancienne architecture vers la nouvelle et alimentation des datasets par des topics Kafka Mise en place de datasets calculés Réécriture de modèles de fraudes existantes et création de nouveaux modèles de fraudes ENVIRONNEMENT TECHNIQUE Cloudera, Hadoop, Spark, Hive, Kafka, PostgreSQL, Scala, Gitlab, Jenkins, Maven, Linux, Kerberos, Scrum

REALIZATIONS Build Rest API that served data to the front-end Built internal tool to automate QA and monitoring of donor-facing web-app Reviewed code and conducted features testing Migration data on AWS TECHNICAL ENVIRONMENT Python, SQL, AWS, Git, CI/CD, Linux