Part of the Primis "Built for Scale" series: how high-growth technology companies structure their teams at every stage.
Every data function starts the same way. Someone is manually pulling spreadsheets, the engineering team is fielding ad hoc data requests they never signed up for, and the founder is spending two days putting together an investor update that should take twenty minutes.
At some point the pain becomes impossible to ignore and the first data hire gets signed off. What happens next, specifically who you hire and in what order, determines whether your data function becomes a genuine advantage or an expensive source of confusion.
Here is what good looks like at each stage.
Under 50 people: your first data hire
You have data everywhere and trust in none of it. Different teams are working from different numbers. The engineering team has become the de facto data team and everyone is frustrated about it.
Make your first hire a data engineer or analytics engineer, not a data scientist and not a lone analyst. You need someone who can build a trustworthy pipeline and a clean model layer before you need someone to do anything clever with the output, because clever analysis on top of broken data is just a faster, more expensive way to be confidently wrong in front of the board.
By the end of their first six months you should have a single source of truth for your key metrics, a cloud warehouse (Snowflake or BigQuery are the most common choices at this stage), and a clean transformation layer in dbt.
The mistake to avoid: Hiring a data scientist first. Without reliable infrastructure they will spend most of their time doing data engineering, which they did not sign up for and will not stay for.
50 to 150 people: building the foundation
The foundation is in place but one person cannot keep up. Stakeholders are multiplying, the data model is straining, and slow data is becoming a real business cost.
This is where the team takes shape. If your first hire was a data engineer, you now need an analytics engineer, and vice versa. You need both disciplines before this stage is done. Add a data analyst embedded in product or commercial, and start thinking seriously about a Head of Data. Not a manager of two people, but someone who can set technical direction and handle senior stakeholder relationships before the function becomes purely reactive.
By this point you should have self-service BI that lets non-technical teams answer their own questions without filing tickets, and the beginnings of an experimentation framework if product-led growth is part of your model.
The mistake to avoid: Treating data as a support function. Companies that under-invest in data leadership at this stage arrive at 200 people with a fragmented team, competing priorities, and a backlog they cannot dig out of.
150 to 400 people: specialisation begins
The data function is established but under pressure from every direction. Product wants faster experimentation. Finance wants tighter forecasting. The ML team wants reliable feature pipelines. Leadership wants a single version of the truth and is still not getting it.
This is where specialisation begins in earnest. A dedicated data platform engineer focused on reliability and cost. Data scientists, now that the infrastructure can actually support them. Analytics engineers embedded in specific business units. The Head of Data evolves into a VP of Data with a genuine seat at the leadership table.
The team also needs to start building proper governance, data lineage, and observability at this stage. Not because it is interesting, but because without it the cracks become very visible very fast.
The mistake to avoid: Letting the team grow without growing its influence. A large but reactive data function that is still explaining why the numbers do not match is a hiring and retention problem waiting to happen. The best data people leave for places where the function has real leverage.
400 people and beyond: data as a strategic asset
At this scale, data is a strategic asset and the organisation knows it. The question now is governance and scale. How do you keep what has been built from becoming a bottleneck?
This is when you need a Chief Data Officer with genuine executive authority, dedicated teams by domain (platform, analytics, science, ML engineering), and data quality functions that are proactive rather than reactive. The CDO at this stage needs to operate at board level and translate data strategy into business outcomes. Technical excellence alone is not enough.
The mistake to avoid: Promoting a brilliant engineer into a leadership role they were not set up for. Past a certain scale, the strategy and stakeholder load becomes a completely different job. Losing a great engineer to a management role they did not want is an avoidable mistake.
A few principles that hold at every stage
Hire infrastructure before insight. The order that works most of the time: data engineer, analytics engineer, analysts, a lead, then data scientists, then governance as scale demands. Deviating from this is possible but should be a deliberate choice, not a default.
Do not under-title the leadership role. The person running your data function at 150 people and beyond needs authority that matches their accountability. A Head of Data with no seat at the table is a structural problem, not just a compensation one.
And invest in data culture as early as the tooling. The best data functions are not defined by their stack. They are defined by whether the business actually trusts the data and uses it. That starts with the first hire.
Thinking about your next data hire?
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