Data pipelines and models, built to run in production.
We staff senior data and ML engineers who turn data into something your product can rely on. Pipelines, platforms and models in production, in European-hours, inside your stack.
What our data engineers cover.
Data your product can rely on — from ingestion and platforms to models in production and the governance around them. Reliability and operability first, not modelling in isolation.
Data pipelines
Batch and streaming ingestion, transformation and orchestration you can trust.
Data platforms
Warehouses, lakes and modelling designed for analytics and ML at scale.
ML in production
Training, serving and monitoring — models that keep running, not notebooks that don't.
Quality & governance
Data quality, lineage and observability treated as first-order concerns.
The judgement we screen for.
Seniority is not years on a CV. It is whether the pipeline still runs, and the number is still right, six months after the demo. This is what we assess.
Production, not notebooks.
Pipelines and models designed to run and keep running — deployed, monitored and owned, not a prototype that works once on a laptop.
Data quality first.
Reliability treated as a first-order concern. Validation, tests and freshness checks, because a wrong number is worse than a missing one.
Operable and governed.
Lineage, access and observability built in, so what ships can be maintained, audited and trusted long after launch.
Three ways to bring data capacity in.
The same technical bar applies across all three. What changes is how much you keep on your side, and how much we run.
Staff augmentation
Add senior data and ML engineers to your existing squad.
Dedicated team
A data-led squad around your platform and models.
End-to-end delivery
We own a data workstream with governance.
Inside your data stack, not adjacent to it.
Your warehouse, your governance and your review standards. Engineers work to your patterns from onboarding, with a named lead and the visibility set out in how we work.
Events → streaming pipeline
Data rarely ships alone.
The strongest results come when data ships next to the capabilities around it — built to the same senior bar, by teams that already work together.
A good fit when you need data and ML that actually reach production and stay reliable, in European-hours, under your governance.
Questions teams ask.
What is the difference between Data and AI and AI engineering?
Data and AI covers data engineering and ML in production. AI engineering covers LLM integrations, RAG and agents, and the governance around them.
Do you get models into production, not just prototypes?
Yes. We staff for production reliability and operability, not only modelling.
How fast can someone start?
Indicative shortlist in 2 to 4 weeks, depending on role and stack.
How do we keep control?
Engineers work in your data stack, under your governance, with a named lead. See how we work.
Get data and ML into production.
Tell us the stack and the gap. We map the right model and the right engineers.