Business Challenge
The client was scaling Data Engineering and AI consulting teams across several concurrent client engagements at once, needing specialists across a genuinely broad spread of modern data tooling rather than one narrow skill set. Treating that as a single "data" requisition would have meant every hire was a compromise on at least one dimension of the tooling stack.
Our Solution
We built dedicated recruitment squads split by specialism, AI, ML, Snowflake, Databricks and Data Engineering, so sourcing for each engagement drew on recruiters who already spoke that tooling's language, instead of a single generalist team covering all of it. That specialisation is a large part of why first profiles went out within 12 hours of a requirement opening.
Our Approach
Five separate recruiting tracks, AI, ML, Snowflake, Databricks and Data Engineering, each run by recruiters fluent in that specific tooling, not one generalist data-hiring team.
Each client engagement's actual tooling and maturity level shaped the brief, rather than defaulting to a one-size-fits-all "Data & AI consultant" spec.
First profiles typically went out within 12 hours of a requirement opening, keeping the Managing Director's pipeline visibly moving from day one.
Structured offer-to-join engagement is what pushed the candidate joining ratio to 95% across a 9-month build-out.
Client Voice
“This was the first time a staffing partner brought us someone who could speak Databricks-specific detail in the first call.”
— Managing Director – Data & AI
The Outcome
By keeping each specialism on its own track, the Center of Excellence scaled without diluting depth in any one tool, 98% of positions were filled against the original technical spec, not a watered-down version of it.
