Data Engineer (AWS / Snowflake / DBT)

About Us

You are not joining a classic data project.

You are joining a rapidly accelerating Data & AI hub, which builds the technical foundations behind AI use cases already used in production by major industrial clients (including pharma).

The challenge is not to "do data".

👉 The challenge is to make AI reliable, usable, and industrializable.

Without you, AI hallucinates. With you, it becomes a business product.

🚀 What you will actually build • Critical data pipelines (ingestion → transformation → exposure) • Snowflake architectures used for real-time AI use cases • Data models designed to reduce LLM hallucinations • Clean, scalable, and industrializable datasets • A data layer that directly feeds business AI products • Modern cloud environments (AWS-first)

🧠 Real Stack (modern and production-oriented) AWS, Snowflake, DBT, advanced SQL Openness to GCP / Azure / Databricks ecosystems

🔥 What makes this position different from the market • You work on AI use cases already in production (not POCs) • Your pipelines directly feed products used by business teams • You are at the heart of the AI value chain (not an "isolated technical component") • Strong proximity to AI / GenAI teams • Modern stack, oriented towards real performance and scalability • Human-scale environment → immediately visible impact

📈 Why it's interesting now The hub is structuring itself around a clear ambition: 👉 move from AI experimentation to large-scale industrialized systems

Concretely: • strong growth of Data & AI teams • projects already funded and in production • rapid scaling logic • opportunities for evolution towards architecture or AI engineering

👤 Profile sought • 2 to 6+ years in data engineering • Very good SQL + modeling level • Cloud experience (AWS, GCP or Azure) • Modern data sensibility (quality, structuring, performance) • AI curiosity strongly valued