Senior-only data & AI staffing
Senior data engineers and forward-deployed engineers for the initiatives that can't stall.
S/4HANA data migrations, lakehouse builds, and AI programs run on data talent that’s shipped before. We place the people who have.
Data initiatives don't fail on platforms. They fail on people.
The migration stalls, the pilot never reaches production, the dashboards nobody trusts — underneath each one is a team that hadn’t done it before. Junior data hires and keyword-matched resumes don’t survive contact with a real migration.
We staff the other kind: senior engineers who have carried a migration, run a production pipeline, or shipped an AI system inside a real enterprise.
What we staff
Data engineers
SAP data migration (S/4HANA, BW, Datasphere) and the modern stack (Snowflake, Databricks, dbt, Airflow). Senior only; people who have carried a migration or a production pipeline.
Forward-deployed engineers
Embedded in your team, shipping on your platform, measured on outcomes. For AI and data initiatives where you need builders, not advisors.
Analytics engineers & data architects
The supporting cast that makes the core team effective — modeling, architecture, and the platform judgment that keeps pipelines trustworthy.
What “forward-deployed engineer” actually means
A forward-deployed engineer (FDE) is a senior engineer embedded inside your team who ships production code in your environment — post-sale, on your data and AI platforms — and owns the outcome until it works. The Palantir-style model, staffed as a service.
Not a sales engineer. They don’t demo — they build.
Not a solutions architect. They don’t hand you a diagram — they ship the system.
Our vetting bar: shipped production code in someone else’s environment — RAG systems, agents, data pipelines, inside a real enterprise under real constraints.
An honest note: FDE staffing is a new practice for us. Early traction, proof points being collected — and we’ll tell you exactly where each engineer has shipped before you ever see a resume.
If any of these sound familiar, call us.
An S/4HANA migration with a data workstream nobody staffed.
A lakehouse or platform migration stalling for lack of hands that have done one.
An AI initiative stuck at pilot because the data isn’t ready.
“We bought the platform; now we need people who’ve shipped on it.”