Data has always fascinated me, not just the numbers, but what happens behind the scenes: how it flows, how it transforms, and how the right pipeline can turn raw information into real decisions.
I started with Cloud & DevOps, and that foundation gave me something valuable. I know how to build systems that actually hold up. Now I'm channeling that into data engineering, where I want to spend the rest of my career.
Cloud & Data
DevOps & Infrastructure
Languages
Built an end-to-end streaming pipeline ingesting telemetry from multiple concurrent IoT sensors into Azure IoT Hub, processed through Stream Analytics using 5-minute tumbling windows, persisted in Cosmos DB for live querying and Blob Storage for historical archiving, and visualized on a Node.js dashboard with real-time safety status across all monitored locations.
Designed and implemented a fully automated CI/CD pipeline provisioning Azure Kubernetes Service infrastructure with Terraform and deploying a containerized application through 5 GitHub Actions workflows, covering static analysis, security scanning, linting, planning, and promotion across test and production environments.
Designed and implemented a document approval workflow twice in parallel: once with Azure Durable Functions (Python) and once with Logic Apps + Service Bus, producing a comparative analysis of both orchestration approaches across reliability, cost, and developer experience.
Industry-sponsored team project involving enterprise knowledge retrieval. Designed and implemented components of a RAG solution integrating internal data sources with large language models.
📧 manfouogabriel00@gmail.com 💼 linkedin.com/in/gabriel-manfouo