Your first data hire vs a consultancy: how to decide
Most seed to Series B companies hit the same wall: the board wants numbers, the dashboards disagree, and nobody owns the data. You can hire, bring in outside help, or do both in sequence. Here's how to think it through.
The real question: what has to be true in 90 days?
Before comparing options, write down what "done" looks like: which metrics must be trusted, by whom, and by when. Board-ready revenue and retention numbers in one quarter is a very different brief from "build us a data team".
Figure 1. One direction of flow and one definition per metric, so the dashboard and the AI give the same answer.
Side-by-side
Figure 2. The fix isn't more dashboards: it's one tested definition of revenue, in the semantic layer, that every dashboard and AI tool reads.
Criterion
Hiring a senior analytics engineer
Consultancy / fractional
Monthly cost
Salary, benefits and recruiting
Fixed fee or monthly plan; varies widely by provider
Time to start
Months (search, notice period, onboarding)
Days to weeks once access is set up
Who does the work
Your new hire
Depends on provider; ask who exactly
Commitment
Ongoing employment
From one-off projects to month-to-month
Knowledge retention
Stays in-house
Only if code and docs live in your repo
This is a qualitative comparison; your own numbers will vary.
Figure 3. You own everything from day one; the consultancy's work ends in a handover the hire can build on, not a dependency.
When hiring first makes sense
Data is core to the product, not just reporting
You have a clear, ongoing backlog that justifies a full-time role
Someone senior can manage and mentor the hire
When outside help first makes sense
You need trustworthy metrics this quarter, before a hire could ramp up
You don't yet know what the role should look like
Your first hire would otherwise inherit an untested, undocumented mess
The hybrid most teams end up with
Get a foundation built first (warehouse, tested dbt models, shared metric definitions, one trusted dashboard), then hire someone to own and extend it. Your hire starts on day one with a working, documented project instead of spending months on cleanup.
Figure 4. Trust comes before dashboards: the first one ships around day 45–75, on tested models and agreed definitions.
What to insist on, whichever route you pick
All code in your repository and your cloud
Every change shipped as a reviewed pull request, with tests and docs
Written metric definitions your team and BI tools share
Least-privilege, read-only-by-default access, revoked the same day work ends
Baked Labs is built for companies that need trustworthy metrics now, before or alongside their first data hire. The usual first step is a fixed-price Data Foundation Audit (7–10 days) that ends in a 90-day roadmap, including a "DIY" option if hiring is the right call.