Home / Our roles / FDE as a service

FDE as a service

A data engineer working alongside your teams

Your data already exists: in business tools, spreadsheets, exports someone copies by hand. An engineer joins your data and BI teams to bring it together, make it reliable and put it to work, on your real systems.

  • On your real sources
  • With your data and BI teams
  • Governance from day one

Your AI diagnostic Book a meeting

Key challenges

Data teams often spend more time hunting for and fixing data than analysing it. The problem is almost never a lack of data, but its dispersion, its inconsistency from one source to another, and the absence of a clear owner.

  • Scattered sources

    Every business tool holds its own version of the truth, with no shared reference. Reconciling two sources becomes manual work that gets redone every time.

  • Fragile pipelines

    Improvised extracts run without supervision and break silently. A wrong figure reaches a dashboard before anyone notices.

  • Uncertain quality

    Duplicates, empty fields, formats that diverge between systems: nobody quite knows what can be trusted. Every analysis starts with a manual check.

  • Data not ready for AI

    An agent or a model is only as good as the material it is given. Scattered, poorly named or ungoverned data rules out any reliable use of AI.

What the engineer does on your site

  • 1

    Map the sources

    Identify where the data actually lives, including outside official systems, and document what exists before proposing anything.
  • 2

    Build the pipelines

    Set up continuous collection from your existing tools, replacing manual exports and re-typing.
  • 3

    Harden the data

    Put in place verifiable quality rules, stable identifiers across systems, and make failures visible instead of letting them pass silently.
  • 4

    Prepare the ground for AI

    Centralise and document data before any agent or model project, so that AI works on material that is reliable and traceable.

What stays in your hands

  • A documented data reference

    The entities that matter for your activity, their relationships, and what must stay unique. Your teams know where to look and what to trust.

  • Pipelines running under supervision

    Collection and transformation chains that work without manual intervention, with alerts when something fails.

  • Dashboards you can rely on

    Indicators built on verified data, rather than extracts rebuilt by hand for every request.

  • A team able to run the system

    The operating documentation and skill transfer needed for your teams to keep control after we leave.

Expected results

  • Less time lost hunting for data

    Data and BI teams find information in the right place, without a file hunt.

  • Trust restored in the figures

    A dashboard questioned at every meeting becomes a reference people can decide on.

  • Ground ready for AI

    The agent or automation projects that follow start from data that is already centralised, instead of stumbling on it.

How we move forward

  1. 1

    Observation

    The engineer settles in with your data and BI teams and watches how data actually flows, beyond what the documentation says.
  2. 2

    Scoping

    Priority sources and use cases are chosen with your teams, starting from the manual task that costs the most to redo.
  3. 3

    Building in batches

    Each delivery adds a hardened source or a running pipeline, put into the hands of those who will use it.
  4. 4

    Handover

    Documentation, training and governance rules are passed on to your teams, so they keep control of the system.

Who it is for

This role suits data and BI teams that need to reconcile scattered sources, organisations that want a shared reference in place before starting with AI, and those whose existing pipelines only hold together because someone thinks about them every week.

In the same family

Frequently asked questions

Do we need a data warehouse before starting ?Not necessarily. We start by mapping what exists and hardening the most critical flows. A data warehouse can come later, if it answers an identified need, not as a systematic first step.
How do we prepare our data for AI ?By centralising and documenting what exists first. An agent or a model connected to scattered or poorly named data produces unreliable answers, whatever its quality.
How do you guarantee data quality ?Through verifiable rules set from the design of the pipelines: expected formats, stable identifiers, alerts on failure. Quality is built into the pipeline, not corrected afterwards.
Who maintains the pipelines after you leave ?Your teams, with operating documentation and training planned from the start. The goal is a system you can run without us.
Where is our data hosted ?In France or Europe depending on your constraints. When sensitive data must not leave, we favour open processing and models run on infrastructure you control.

Let's talk about your context

We always start by understanding the real work. One conversation is enough to know whether we can help, and how.

Tell us what you need Book a meeting