Every growing company hits the same inflection point. The CEO can’t get a straight answer about margins. The finance team is spending Friday afternoons reconciling spreadsheets. Someone says, “We need a data person.”
The question is: what kind?
The Full-Time Data Hire
The instinct is to post a job listing. “Data Analyst” or “Data Engineer” — companies at this stage usually aren’t sure which one they need, which is itself a signal.
Here’s what a full-time data hire gets you:
Institutional context. Someone who learns your business deeply over months. They know that “Region 3” actually means the Southeast minus Florida. They know that the sales team logs deals differently than the marketing team tracks leads. This tribal knowledge is genuinely valuable.
Daily availability. They’re there when things break. They can join the Monday meeting and answer questions in real time. They build relationships with department heads.
Long-term ownership. They maintain what they build. No handoff gaps, no “the consultant left and nobody knows how this works.”
And here’s what it actually costs:
A competent data analyst runs $70K–$95K base in most markets. A data engineer is $100K–$140K. Add benefits, payroll taxes, equipment, and management overhead, and you’re looking at $100K–$200K fully loaded per year.
But cost isn’t the real risk. The real risk is scope.
The Scope Problem
A single data hire at a $10M company is going to be asked to do everything: build pipelines, design the warehouse, create dashboards, write SQL for ad-hoc requests, evaluate tools, manage vendor relationships, and probably also fix the Wi-Fi because they’re “the tech person.”
Nobody is good at all of those things. The analyst who builds beautiful Tableau dashboards usually can’t architect a data warehouse. The engineer who can build bulletproof pipelines in Airflow usually isn’t the person you want presenting to the board.
What happens in practice is one of two outcomes:
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You hire an analyst. They build dashboards on top of messy data. The dashboards look good but the numbers are unreliable. Six months later, you’re in the same spot — nobody trusts the data, but now it’s prettier.
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You hire an engineer. They build solid infrastructure but it takes 6–9 months before anyone sees a dashboard. Leadership loses patience. The engineer gets pulled into ad-hoc requests, the infrastructure work stalls, and you end up with half-built systems.
The Fractional Alternative
A fractional head of data gives you senior-level expertise on a retainer — typically 15–30 hours per month. Here’s when it makes more sense than a full-time hire:
You need strategy before execution. If you don’t know what your data architecture should look like, hiring someone to build it is premature. A fractional consultant maps the landscape, designs the architecture, evaluates tools, and creates a roadmap. Then you know what role to hire for.
You need a range of skills. Building a data function from scratch requires architecture design, pipeline engineering, analytics, governance, and vendor management. A senior fractional consultant has done all of these across multiple companies. A single mid-level hire has done maybe two.
You need speed. A fractional consultant can be productive in week one. They’ve seen your situation — or something very close to it — at three other companies. A full-time hire needs 2–3 months just to understand the landscape before they start building.
Your budget doesn’t support a senior hire. A VP of Data costs $180K–$250K. A fractional engagement runs $3,000–$8,000 per month. You get the strategic thinking at a fraction of the cost — literally.
When to Hire Full-Time Instead
Fractional isn’t always the answer. Here’s when full-time is the right call:
You already have clear requirements. If you know exactly what needs to be built — specific pipelines, specific dashboards, specific integrations — and you need someone maintaining it daily, that’s a full-time role.
You have enough work for 40 hours per week. If your data needs genuinely fill a full-time schedule (not including ad-hoc busywork), the economics shift toward hiring.
You have someone to manage them. A junior or mid-level data hire without technical leadership is set up to fail. If you have a CTO or VP of Engineering who can provide direction, a full-time hire makes sense. If you don’t, consider a fractional lead first.
Data is your product. If you’re a SaaS company where data is central to your offering, you need full-time data people. Period.
The Hybrid Path
What I recommend most often — and what I’ve seen work best — is a phased approach:
Phase 1: Fractional strategy (months 1–3). Bring in a fractional consultant to audit your current state, design the target architecture, build the first critical pipelines, and define the role spec for your first hire. This typically costs $10K–$25K.
Phase 2: First hire + fractional oversight (months 3–9). Hire a data analyst or engineer based on the role spec from Phase 1. Keep the fractional consultant for 10–15 hours per month to provide mentorship, architectural guidance, and quality review.
Phase 3: Independent team (month 9+). By this point, your hire understands the systems, has documentation and architecture to work from, and can operate independently. The fractional engagement scales down to occasional advisory.
This path costs more than just hiring someone on day one. But it dramatically reduces the risk of a bad hire, a mis-scoped role, or six months of work that has to be redone.
The Question Behind the Question
When a company asks “Should we hire a data person?” they’re really asking “How do we stop making decisions on incomplete information?” That’s a strategy question, not a staffing question.
The right answer depends on where you are, what you need, and how quickly you need it. But the wrong answer is almost always “post a job listing and hope.”
Not sure which path is right for your company? Let’s talk. I’ve been the full-time hire, the fractional consultant, and the person advising on which one to choose. Happy to share what I’ve seen work.