Younes M.

Data engineering, for me, is about one thing: making sure the data your team relies on actually holds up under pressure.

Over the past two years I've built and shipped production data systems for banking, SaaS, and AI-driven platforms, NiFi and Spark pipelines handling millions of daily transactions, Kafka streams for real-time processing, Airflow orchestrating it all, and dbt/Snowflake/BigQuery turning raw data into something analysts and decision-makers can actually use.

Right now I'm deep in AI/LLM integration work, connecting data pipelines to production inference systems (vLLM) for a European SaaS platform. It's given me a front-row seat to what "AI-ready data" actually requires: clean ingestion, solid orchestration, and zero tolerance for silent failures.

I work with Docker, Kubernetes, GitLab CI/CD, and Terraform, not because it's trendy, but because pipelines that only work on my laptop aren't worth building.

What you get working with me: pipelines that are documented, monitored, and built to survive contact with real-world data — not a proof of concept that breaks the moment volume scales.

Have a data challenge you're trying to solve? Let's get on a call — I'll tell you honestly whether it's a quick fix or a bigger build.