Data Leadership.
Directors of Data Engineering, Heads of Data, VPs of Data and AI, AI Deployment Leaders. The hires who decide what gets built, what gets bought, and whether the data organization earns the trust of the rest of the company.
Most failed data leadership hires were scoped wrong before the search even started.
Data leadership titles hide three different jobs. A platform leader builds infrastructure and engineering teams. An analytics leader builds the reporting and decision-support function the business runs on. An ML and AI leader takes models and AI capabilities into production. Some companies genuinely need one person across all three; most need one of the three and write a job description that asks for everything. The candidates who claim all three fluently are often strong in none, and the strong specialists quietly pass on a req that reads confused.
Scoping the role honestly is the highest-leverage hour of the entire search. What should this leader own in the first 90 days: stabilizing what exists, hiring a team, delivering a specific capability, or fixing trust with the business? What build-versus-buy calls will land on their desk in year one, and does your budget reality match the candidate profile? Is this a first data leader who still writes code, or an org designer inheriting forty people? Each answer changes who you should be talking to.
Org design judgment is the quiet differentiator. Where data engineering reports, how analytics relates to platform, whether ML sits inside or beside the data organization: leaders who have made those calls before, and can tell you honestly which ones they got wrong, are the ones who succeed. Titles alone will not surface that; structured references from people who lived under those decisions will.
These searches run engaged for a simple reason: the qualified pool is small, entirely passive, and talks to a recruiter only when the outreach is credible and specific. As part of a practice dedicated to the technical talent that turns AI and data investments into outcomes customers actually see, we run data leadership searches with the same dual screen we use everywhere else: technical credibility first, then evidence the person can lead through other people.
Why Platforms Choose Engaged Search
Where the search lives.
Senior leadership and niche-headhunter roles. The recruiter assigned to your search has spent a decade in this segment.
ENGINEERING LEADERSHIP
AI DEPLOYMENT LEADERSHIP
Data Leadership, specifically.
The questions hiring operators send in the first call.
What data leadership roles do you place?
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Directors of Data Engineering, Heads of Data and Data Platform, VPs of Data and AI, Chief Data Officers, AI Deployment Leaders, and Directors of ML Engineering. The common requirement across all of them: technical credibility that survives contact with the engineers they will lead, plus real evidence of leading through other people.
How do we decide between a platform, analytics, or ML leader?
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Follow the pain. If pipelines break and infrastructure cost is out of control, you need a platform leader. If the business cannot get trustworthy answers, you need an analytics leader. If models never reach production, you need an ML or AI deployment leader. If all three hurt, hire for the one that blocks revenue first and let that leader help scope the rest. We work through this with you at scoping.
What should a data leader own in the first 90 days?
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One primary mandate, stated before the search starts: stabilize what exists, build the team, deliver a specific capability, or rebuild trust with the business. Leaders fail most often when the company privately expects all four at once. Setting the 90-day mandate up front also sharpens every interview, because candidates can respond to something concrete.
Should the first data hire be a leader or an engineer?
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It depends on what exists. If there is no data function at all, a hands-on leader who still builds, typically a director-level athlete, beats either a pure executive or a lone engineer. If a team already exists and is drifting, hire the leader first and let them shape the next hires. Hiring a senior executive to manage zero people is the most common version of getting this wrong.
Why do data leadership searches run engaged rather than contingent?
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Because the pool is small and fully passive, and the evaluation is deep: technical credibility, org design judgment, and structured references take real recruiter hours before a single introduction. Engaged search runs 25 to 35 percent of first-year compensation with a deposit credited against the final fee; contingent, at 18 to 25 percent owed only on placement, rarely fits this level. Replacement guarantee terms are agreed in writing at the start of the search.
Do you help with org design questions during the search?
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We give you a straight read on how comparable companies structure the function, because scoping the role forces those conversations. We are recruiters, not consultants; the value we add is telling you honestly when the role as scoped will not attract the leader you want, and what change would fix it.
Three paths from here.
You are still shaping what the data organization should look like.
Start at the practice pillar. It maps the full talent landscape this leader will hire from, from Forward Deployed Engineers to platform and applied AI roles, which is useful context before you define the leadership seat.
Read the AI & data talent pillar →The leader will need to hire engineers fast after landing.
Read the data engineer hiring guide. Knowing how the engineering searches will run helps you scope a leadership profile that can actually attract and close those engineers.
Read the data engineer hiring guide →The mandate is clear and the seat is open.
Book a scoping call. We pressure-test the scope, recommend the engagement structure, and come back with a fee quote and a read on the leadership market for your segment.
Start the scoping call →Scope the mandate. Then meet the market.
Tell us what the leader will own: platform, analytics, ML, or all three. We pressure-test the scope on the scoping call, then recommend the engagement structure and give you a market read.