Cedric Fowoue Woutbe

I work in a team that provides Oracle as a Service. An API is in place to allow clients to provision and manage clusters and databases. The technologies used are Python, Flask, FlaskAdmin, Airflow, Kubernetes, Jenkins, Github Actions, Jfrog Artifactory.

I worked in a DevOps team that manages and supervises API Management. The missions involved conducting feasibility studies with POCs to validate the migration project from WSO2 to Kong.

Tasks performed included:

- Kong deployment architecture: defining component separation, communication between components, component instantiation, and observability.

- Kong deployment in Sandbox: defining a process via Gitlab, Gitlab CI, ArgoCD, Vault, Helm, Kustomize, and Kubernetes for instantiating product onboarding environments.

- API integration into Kong: automating the integration of different endpoints and backends with protection and supervision mechanisms from Swagger or OAS.

- WSO2 to Kong migration: extracting APIs from WSO2 and integrating them into Kong.

- Observability: implementing MELT (Metrics, Events, Logs, Traces) reporting.

- Extending Kong functionalities using plugins: setting up boilertemplates for developing Kong plugins in JS, Python, Go, and Lua.

Technologies used were Lua, Python, Bash, NodeJS, Go, ArgoCD, Kubernetes, Hashicorp Vault, Kong, WSO2, Kafka, Opentelemetry, Dynatrace, Grafana, Prometheus, Helm, Kustomize, ELK.

Implementation of infrastructure provisioning automation chains.

• Implementation of a self-service platform for provisioning data science tools (Mlflow, Vscode, Jupyter, RStudio, RShiny, PySpark) with associated infrastructure.

• Deployment of data science tools on Kubernetes via the Backend API - Python.

• API Development (Python backend – flask/fastapi):

o Hashicorp Vault exploitation via API.

o CRUD operations on Kubernetes objects.

o PostgreSQL database provisioning.

o CRUD operations on S3 buckets via API.

o LDAP authentication.

• Development of metrics exploitable by Prometheus.

• Support for data scientists in using Dataiku (SQL, Datasource, python, pyspark, etc.).

• Application testing (pytest).

• Building Jenkins pipelines for constructing Jenkins slave images.

• Building Jenkins pipelines for constructing application images.

• Building Jenkins pipelines for validating Python code.

• Building Jenkins pipelines for deploying applications.

• Development of shared libraries (Groovy).

• Deployment on Kubernetes (On-Prem and Azure).

• Construction of application images.

• Writing Ansible playbooks.