Senior analytics & ai engineer

Jeune société de conseil et d'ingénierie, ALPEE accompagne des entreprises industrielles du bassin Alpin sur des missions d'assistance technique et d'expertise.
Voici nos ambitions :
Etre proche des clients, en créant un vrai partenariat long terme avec les entreprises locales (dpt 38,73,74 uniquement)

Etre proche des collaborateurs en leur proposant des projets locaux et en les impliquant dans les décisions de ALPEE.
Si vous vous retrouvez dans ces ambitions, alors ALPEE est fait pour vous !

About the role

We are looking for a Senior Analytics & AI Engineer who genuinely lives at the intersection of
Analytics Engineering and AI — someone who understands that the quality of data models is
what makes or breaks an AI product, and who knows how to build both.
This is a hands-on technical role with a strong Analytics Engineering foundation and a growing
AI dimension.

What you will do


1. Analytics Engineering — the core of the role

- Own and evolve the semantic layer: curated, documented, and tested dbt models that serve BI, self-service analytics, and ML feature needs
- Define and maintain KPI definitions across business domains (Sales, Marketing, Finance, Supply Chain, eCom) — the single source of truth the whole organization relies on
- Drive data quality, documentation, and observability practices — a broken data contract is treated like a bug in production
- Collaborate with Data Engineers on pipeline design and data availability, and with the Data Scientist on feature engineering and model readiness
- Contribute to the semantic layer evolution roadmap as part of the SPINE program

2. AI & Agentic — where we are heading

- Contribute to the Agentic AI POC on eCom and Marketing insights ("ChatGPT for Data") — help design what data needs to look like for an agent to reason on it• Support the Profit Margin Agent use case: from data preparation and structuring, to integration
- Help establish MLOps practices on Azure ML: model lifecycle management, monitoring, deployment standards — so the Data Scientist can ship with confidence
- Evaluate AI tooling pragmatically — bring a critical, grounded view on what fits our stack and our maturity level
- Document AI patterns and architectural decisions as we discover them, building shared knowledge for the team

3. Technical Vision & Team Contribution

- Bring informed technical opinions: propose architectural decisions, evaluate tools, challenge choices with well-reasoned arguments — while staying pragmatic
- Keep up with the field (models, frameworks, patterns) and bring back what is genuinely relevant to our context — signal, not hype
- Mentor Analytics Engineers: share best practices, run code reviews, raise the bar on modeling standards
- Contribute actively to PI Planning, sprint reviews, and architecture discussions — not just executing tickets, but shaping what we build
- 5+ years in Analytics Engineering, Data Engineering, or a similar role with strong data modeling responsibility
- Proven track record delivering production-grade dbt models and semantic layers in a complex data environment
- Hands-on experience with AI or ML tooling in a data context — not necessarily deep ML expertise, but genuine curiosity and practical engagement
- Experience working in cross-functional environments, collaborating with both technical and business stakeholders