PhD. Richard E. EWELLE

- Developed scalable data ingestion pipelines to integrate raw data from sources including S3, Salesforce, SharePoint, Facebook Ads, Google Ads, and on-premises ERP systems into Redshift. - Designed and implemented ELT pipelines for batch processing of procurement, sales, and marketing data using Redshift and SQL. - Designed, implemented, and administered a Kubernetes cluster using Amazon EKS on AWS, for Dagster and Airbyte deployment - Built and maintained CI/CD pipelines using GitHub Actions to automate continuous deployment on Kubernetes. - Contributed to the migration of ELT pipelines from Redshift to Snowflake. - Transitioned workflow orchestration from a custom-built TypeScript solution to Dagster.

Core Services: - Data Warehousing (Redshift, Snowflake, BigQuery) • NoSQL Database (Elasticsearch, Mongodb, Firestore) - ETL/ELT Pipelines & Workflow Orchestration • Cloud Solutions (AWS, GCP) • Kubernetes & CI/CD Expertise

Contributed to the development of a PaaS solution on GCP (BTDP), providing a catalog of services to BTDP users, leveraging FastAPI, Flask and Terraform. - Delivered secure and compliant access to L’Oréal’s data systems by defining attribute-based access rules across platforms using BigQuery, Firestore, and Workflows - Enabled IT Organization and IT owner to manage budgets effectively for BTDP projects by integrating Billing, Pub/Sub, and Notifications. - Designed workflows for seamless data import/export leveraging BigQuery, Cloud Storage, Firestore, Workflows, and Cloud SQL - Streamlined users and group management by synchronizing Azure Active Directory with GCP Cloud Identity to manage group memberships - Facilitated temporary access on resource such as a project, bucket, dataset or group (GoogleProject, CloudStorage, BigQuery) - Simplified access control for BTDP services by optimizing Service Account and Azure app permissions using Firestore and Azure MS Graph integrations.

- Real-time audit log ingestion from multiple Europol applications using Filebeat and other agents. - Enabled audit logging on Elasticsearch and Kibana for robust tracking. - Integrated AM Queue for queuing, Logstash and custom processing logic. - Implemented Elasticsearch Data Streams for scalable indexing and storage. - Built interactive Kibana dashboards, visualizations, alerts, and reporting - Migrated the ELK Stack from version 7.17 to 8.9. - Deployed on VM instances with Puppet, and on a Kubernetes Cluster

- Architecture design and Implementation of a real-time monitoring stack (Postgres, Elasticsearch, Kibana, Kafka, Kafka-connect, Ksql) - Contribution to the implementation of a Datawarehouse for massive data processing in batch and incremental modes (Redshift, DBT, Airbyte, SQL) - Orchestration and workflow management via Nifi and Airflow - Development of data processing scripts (Python, SQL) CI/CD Implementation with GitlabCI, Kubernetes and argoCD Technical environment : • Data : Postgres, Elasticsearch, Kibana, Kafka, Kafka-connect, Ksql, Nifi, Airflow, Redshift, Airbyte, DBT • DevOps: Kubernetes, Docker, Gitlab, ArgoCD, Prometheus, Grafana, Ansible, Linux, AWS. • Languages : Shell, Python, SQL, KSQL Yaml. • Tools : Atom, Cent-OS, ubuntu, pycharm, dbeaver

Ingénieur ELK (ElasticSearch, Logstash, Kibana). • Prise en main de l’existant : Docacloud, le cloud interne de DOCAPOSTE basé sur Openstack. • Création de l’infrastructure cible avec le service Heat. • Installation et configuration des applications avec Ansible • Applications : ElasticSearch, Logstash, Kibana. Environnement technique : • Data : ElasticSearch, Logstash, Kibana • DevOps: Openstack, Heat, Ansible, Linux, Git. • Langages : Shell, Python, Yaml. • Outils et systèmes : Atom, Cent-OS

Expertise Data: MongoDB, apache NIFI - Administration et Développement - Référent technique de l’équipe Data du projet IMPULSE • Modélisation des données NoSQL • Mise en place et administration de l’architecture serveur MongoDB avec multiples réplicas pour chaque environnement (dev, int, reçu, prod) • Mise en place et administration du process de livraison des migrations du schéma de données sur MongoDB : Initialisation et delta • Mise en place et administration du process chargement des données avec Apache NIFI, de DB2 vers MongoDB Environnement technique : - Data: MongoDB, Apache NIFI, ElasticSearch, Kibana, DB2 - DevOps: Linux, Jenkins, Xldeploy, Maven, Git, Docker, Ansible - Languages: Shell, Python, SQL, JSON, JavaScript, Java - Outils: Jira , Confluence, Agile(Scrum)

Software Development around R&D projets and Big Data. Technical environment: - Data: MongoDB, ElasticSearch, Kibana, Hadoop(HDFS), Spark, PostgreSQL, MySQL, PostGis - DevOps: Linux, Jenkins, Maven, Git, Docker - Frameworks: SpringBoot, Django, iOS, Android, Ionic, Swagger - Languages: Shell, SQL, JavaScript, Java, Objectif C, Python, Json - Outils: Mantis , Agile(Scrum)

Design and implementation of a server architecture for serious games in the cloud for low-end devices. Technical environment: • Languages​​: Java, Scala, Ruby, XML, HTML, CSS, Latex • DBMS: MongoDB • Framework: PlayFramework, LibGDX, Apache, Jetty, Slick2D • Systems: Windows 7 / Ubuntu • Tools: Fireworks, SVN, GIT, Maven, Agile (SCRUM)

• Creation of a rehabilitation manager. • Design of a server architecture for serious games. Technical environment: • Languages​​: Java, Ruby, XML, HTML, CSS • DBMS: MongoDB, CouchDB • Framework: RedDwarfServer, Unity3D, Apache, Jetty • Systems: Windows 7 / Ubuntu • Tools: Fireworks RUP, SVN