Data Engineer : The foundation everything else rests on
An AI agent is only worth the data you give it. That is this role.
What you take on
You classify a client source as Bronze, Silver or Gold
And discover that half of it is unusable as it stands. Our model exists precisely for that: surfacing, from the audit onwards, what must be cleaned before an agent relies on it.
You wire in an export that cannot be replaced
The client has an old ERP, nobody wants to change it, and the pipeline still has to run on it every night.
You build the corpus that feeds a RAG pipeline
And test its quality. A badly structured corpus gives wrong answers with the same confidence as right ones. Part of your job: finding those holes before the client finds them in production.
You deploy into the client's cloud account, not yours
Our systems run in the client's infrastructure (data sovereignty, GDPR, AI Act). That means learning their environment (AWS, GCP or Azure) rather than imposing yours.
You tell a client no about the quality of their data
And you propose what comes next. This is not a technical aside: it is a conversation with someone whose dashboard is blocked and who does not want to hear it.
You build a reporting agent that puts a number in front of a decision-maker
Every week, with no manual intervention.
Month 1, you take over the pipelines of a live client and learn how our systems consume the data downstream. Month 2, you own the data layer of a full engagement. Month 3, you step in upstream, during the audit phase, where what is feasible gets decided.
Ce qu'on cherche
- Languages
- Excellent SQL, solid Python
- Data
- Modelling, ETL/ELT, Bronze/Silver/Gold architecture, large volumes
- Architecture
- CRM/ERP integrations, cloud, queries that hold up in production
- Applied AI
- RAG corpora and data quality, reporting agents
- Field work
- Can tell a client their data is not ready, and propose what comes next
- Background
- 3 to 8 years in data engineering
Ce qu'on ne cherche pas
Someone who wants a stable scope. Every client has a different stack.
Where it differs from neighbouring roles
| Role | What they do | Who owns the outcome |
|---|---|---|
| Data Analyst | Queries data that is already clean | Whoever decides from the number owns the outcome |
| Analytics Engineer | Transforms and models inside the warehouse | The data team consuming the models owns the outcome |
| In-house generalist data engineer | One stack, one company | That company's data team owns the outcome |
| Data Engineer, AI Makers | Unifies siloed systems at each client, deploys into their cloud | You own the outcome, on a different stack every engagement |
Remote, in a time zone compatible with Paris and Casablanca (Europe, North Africa or nearby), with no requirement to live in either city, and occasional time in our offices.
Is this role for you?
No cover letter. The form takes five minutes and starts with showing what you have built.
Apply for Data Engineer