Romain Di Giorgio

🌐 Technical leader for an international team of about 10 to 20 members (France, Spain and India) working on a Kubernetes/Openshift native application project to deploy ETL microservices. 🛠️ Technical stack • Openshift / Kubernetes (On Premise and Azure) • Ansible • Java 11/17 with Spring Boot 2.4/3 • Kafka Streams • Jenkins CI / SonarCube 📋️ Tasks • Developing and deploying Kafka Streams workloads • Helping team members and helping them better understand DevOps and more particularly Kubernetes concepts • Implementing CI/CD flows with GitOps for deployments

🌐 Working with FDJ Cloud operations team of about 10 persons on cloud infrastructure administration, automation and delivery. 🛠️ Technical stack • Openstack, AWS • Amazon EKS, Rancher RKE2 (Kubernetes 1.24) • Terraform, CDKTF, Helm, Kustomize • Gitlab 📋️ Tasks • Working on infrastructure automation & delivery processes • Setting up Prometheus federation strategies

🌐 Working on load testing, monitoring and creating auto-scaling features for workloads deployed on Kubernetes in a team of about 5 persons. 🛠️ Technical stack • Kubernetes 1.22.2 • Ansible, Terraform, Kustomize, Helm • Prometheus, ELK • Gitlab, Flux CD 📋️ Tasks • Developing tools to implement load & stress testing for user critical applications • Implement generic & business specific metrics monitoring • Create a config-as-code tool for ELK stack (Python) • Implement logging & monitoring with ELK stack and Beats • Implement Horizontal Pod Autoscaling using custom/external metrics

🌐 Short mission as a backup SRE. 🛠️ Technical stack • AWS EKS / Kubernetes 1.21.5 • Terraform, Ansible • Argos CD, Github Actions • Dagster 📋️ Tasks • Troubleshooting infrastructure and CI/CD issues • Creating Terraform modules (Google Suite, Terraform, GCP providers) • Optimising Dagster deployment on Kubernetes

🌐 Working on a Kubernetes/Openshift native application project in a team of about 20 members (SCALIAN) for Amadeus. 🛠️ Technical stack • Openshift 4.8 / Kubernetes 1.21.1 • Ansible • Java 11 / Spring Boot 2.4 • Kafka Streams • Jenkins CI 📋️ Tasks • Helping migration of workloads to Azure public cloud • Coaching team members and helping them better understand DevOps and more particularly Kubernetes concepts • Implementing CI/CD flows with GitOps for deployments

Big Data is the new oil and retailers have hundreds, sometimes thousands of oilfields remaining untapped: their stores. One after the other, many in-store technologies have been deployed to meet various needs, but the data they generate are not centralized and therefore, cannot be exploited. Yet they allow a better understanding of the customer journey and more efficient interactions with clients and prospects. instore.digital edits ID, the first operating system for the connected store and its ecosystem. ID applications are divided into four main suites: - ID OS, open source, to collect and process the data. - ID Analytics, to visualize and analyze data split into business-oriented dashboards - ID Interaction to interact with customers both in-store and via retargeting. - ID Exchange to ease the exchange of data with the retailer’s ecosystem. The solution enables compatibility between hardware and software both in-store and in the various information systems of the ecosystem. All customer data are centralized in real time and omnichannel in order to build a unified and portable consumer profile. Tech stack: - Docker, Google Cloud, Rancher - Java 8, Maven 3, Spring Boot, Hibernate, JPA - Elasticsearch, Kibana, Kafka, Couchbase Tasks: - Refactoring previous data ingestion pipeline: we received millions of documents each and every days from client IoT objects, processed the data (Kafka / Spark Streaming) and indexed user profiles in realtime (Elasticsearch) for our web application - Working on making IDOS deployable in one-click for our clients (Docker / Rancher) - Migrating workloads into Docker containers and deploying them on Google Cloud Platform

Développement et maintenance d'un logiciel référentiel de test: https://referty.com/en/home/ Technologies employées : - Docker, Google Cloud Platform - JavaEE, Spring, Hibernate, GWT Tâches: - Construction from-scratch de ReferTest: single page application GWT et web-service RPC - Implémentation du cahier des charges basé sur la version desktop de ReferTest - Système de déploiement sur Google Cloud Platform pour exploitation en mode SaaS (Docker) - Maintenance, implémentation de correctifs et prise de contact directe certains clients

TapValue is a startup founded in October 2012, specialized in mobile targeting and cross device retargeting. Tech stack: - Docker, Google Cloud, Rancher - Java 8, Maven 3, Spring Boot, Hibernate, JPA, GWT - Elasticsearch, Kibana, Kafka, Couchbase, Spark Streaming Tasks: - Participated in building a single page application (GWT) to display pre-processed / aggregated data to our clients - Participated in building BigData ingestion pipeline (Kafka / Spark Streaming) and user profile data structures (raw in Couchbase / indexed in Elasticsearch) - Built a proof-of-concept of realtime in-store client notification on approach of IoC objects: the idea was to push discounts to a targeted audience when they walk past or near a physical store