Zero-Gravity.ai
Book a conversation

Join Zero-Gravity.ai · Forward Deployed Data Engineer

Move beyond the platform. Make sure your work moves decisions.

We are looking for medior & senior, hands-on engineers who are able to move between data platforms, business meaning, software, workflows &  operational reality.

Medior to Senior · Antwerp / Europe · Hybrid and client-embedded

Incoming transmissionnow · live
Zero-Gravity.ai
small senior practice · strengthening the core

…has found the gap between data engineering and operational reality.

transmission · role signalsignal · trusted

Most data work stops too early.

The data has been collected.

The pipeline runs.

The model has been validated.

The dashboard refreshes.

And somehow, the decision, process or outcome remains largely unchanged.

A Forward Deployed Data Engineer builds trusted data foundations and carries them all the way into real decisions, applications and workflows.

You follow the work into the organisation, the application, the workflow and the decision.

  • — Organisation
  • — Application
  • — Workflow
  • — Decision
  1. 01

    Understand the decision

    Begin with the outcome, decision or operational problem — not with the available technology. Work with users, domain specialists and technical teams to understand what must become different and what evidence would show that the intervention worked.

    • — Outcome
    • — Decision
    • — Context
    • — Evidence
  2. 02

    Build reliable context

    Design the data structures that make information dependable, understandable and reusable — ingestion, transformation, analytical and operational modelling, semantics, quality, lineage, observability, APIs and cloud platforms.

    • — Pipelines
    • — Models
    • — Meaning
    • — Trust
  3. 03

    Connect information to action

    Ensure useful information does not end in a warehouse, notebook or dashboard. Connect data and intelligence to applications, operational systems, decision support, AI solutions, workflows and accountable human judgement.

    • — Applications
    • — Workflows
    • — Decisions
    • — Integration
  4. 04

    Learn from real use

    Stay close enough to see what happens after deployment. Observe where users hesitate, where exceptions appear, where definitions fail. Carry those lessons back into the model, architecture or process.

    • — Use
    • — Feedback
    • — Exceptions
    • — Learning
  5. 05

    Make the solution reusable

    Do not solve every local problem with another permanent exception. Distinguish a genuine local requirement from a missing reusable capability, a weak definition, a workaround or a structural architecture problem.

    • — Patterns
    • — Capabilities
    • — Reuse
    • — Continuity
  1. Trusted Data
  2. Shared Context
  3. Better Decision
  4. Operational Workflow
  5. Observable Evidence

The role owns the connections where useful information usually loses momentum.

This is not a conventional data-engineering role.

  • You will not receive perfect requirements.

    You will help clarify the problem.

  • You will not disappear behind a backlog.

    You will enter the operational reality.

  • Your work will not end with technical delivery.

    You will look for evidence that something became better.

Your current title matters less than the combination.

You may currently call yourself a Data Engineer, Analytics Engineer, Data Architect, Solution Architect, Technical Consultant or Engineering Lead. What matters is the combination of capabilities you bring.

Data engineering
Strong SQL, Python and production-grade data pipelines.
Architecture and quality
Modelling, cloud platforms, deployment, testing, lineage and observability.
Integration
Connecting data to applications, APIs, workflows and operational systems.
Context
Experience working directly with business, domain and operational users.
Judgement
Structuring ambiguity, communicating clearly and challenging assumptions constructively.

Success is not another completed technical workstream.

We measure the role by what happens after the work is delivered.

Trust
People use information they understand and trust.
Movement
A decision, process or workflow becomes faster or more effective.
Capability
An experiment becomes a dependable production capability.
Reuse
A local exception improves a reusable platform, model or pattern.

The organisation can demonstrate what changed.

You can explain a technical constraint without hiding behind jargon.

You can challenge a business assumption without turning the conversation into theatre.

You can move between architecture and implementation without losing sight of either.

Not a finished corporate machine.

A small senior practice working across data, AI, information, processes and decisions — directly involved with client teams, without a distant sales layer.

  1. Analytics Engineer
    Makes data usable.
  2. AI Engineer
    Makes systems intelligent.
  3. Forward Deployed Engineer
    Makes intelligence operational.

Usable Data → Intelligent Systems → Operational Reality

The Forward Deployed Data Engineer strengthens the connection between usable data and operational reality.

The operating code.

  1. 01
    Start with the outcome

    State what must become different and how that difference will be recognised.

  2. 02
    Start with the decision

    Data and AI create value when they improve a decision and the action that follows.

  3. 03
    Make the necessary data usable

    Information must be understandable, trustworthy and connected to real work.

  4. 04
    Move in meaningful increments

    Take the smallest step capable of creating value or producing evidence.

  5. 05
    Keep human judgement visible

    Technology should strengthen human capability without hiding ownership.

  6. 06
    Prove what changed

    Delivery is not the result. Observable improvement is.

You will have room to influence:

  • —how the role develops;
  • —how forward-deployed delivery works;
  • —which technical patterns become reusable;
  • —how data and AI engineering connect;
  • —how evidence enters delivery;
  • —what Zero Gravity looks like in operation.

Autonomy, but not disappearance. Pragmatism, but not shortcuts disguised as pragmatism. Speed, but not activity without direction.

Send us something useful.

You do not need to write a ceremonial cover letter explaining that you are passionate about data.

Tell us about one thing you helped make work in reality:

  • —What was stuck?
  • —What did you personally do?
  • —What changed?
  • —What became reusable?

Data should not end in a pipeline.

Your work should not end in a handover.

Lighter is the system. Movement is the proof.