Before we dive in, a bit about the authors and why our perspective may be relevant:
, and in his day to day role has spoken with hundreds of data leaders across all company sizes and industries. This gives Marc a unique vantage point on what generally does and doesn’t work for data teams. Marc has also led product teams at Clari and Assembled pre-first data hire and has experienced first-hand and assisted in the set up of the data team
as a startup and you’re starting to think about scale and operational efficiency.
But the question of who you should hire for this role can be complicated. Do you hire a data scientist who can perform some magic on your data and uncover insights no one thought about? Or perhaps a data engineer who can actually clean up the data so that you can even try and start making sense of it?
In this post, we’re going to first touch on what signs you should be looking for that indicate that you might be ready for your first “data hire”, and then once you’re ready to proceed, what experience that individual should come with and what you can realistically expect of them.
When is the right time for your first data hire?
What should happen prior to your first hire
Long before you’ve hired your first data employee, you’ve likely already been doing some form of data analytics, you just haven’t had a person doing it full time. Generally speaking, there are two stages an early stage company goes through before they think about “analytics” as a full-time role:
Basic product & marketing analytics: On the product front, you’re using a product analytics solution that stores events, such as Posthog, Amplitude, Mixpanel or Pendo. This gives you some basic user metrics to help you understand how customers are using your product. In a separate thread, you’ve set up some web analytics (likely in GA4) to start tracking website traffic and attribution, and if you’re following more of a B2B sales motion, you likely have a CRM (Hubspot, Attio or Clarify) that provides basic reports. At this stage, you should be embracing canned reports as much as possible since you’re mostly trying to get directional insights from your data. You likely don’t have too many users or such a large sales pipeline that you don’t already have a good feel for what’s going on. This is also the stage where you can, and should, embrace spreadsheets as much as possible. Your business is evolving so quickly that you should expect what you care about to continually change.
SQL queries = BI: At this stage you want to start digging into more custom reports that are unique to your business. This is when data stops fitting nicely into canned reporting solutions. You may have a certain type of record in your customer database that’s powering your product that you want to better understand. For example, going back to our fictional Superdope company that sells widgets, you may want to see how many customers have more than X widgets in their cart but haven’t checked out. The best way to do this is likely just to write a SQL query against your production data. If you’re just doing one-off queries and are comfortable in a SQL IDE, that will probably get the job done, or if you want to start building dashboards or board reports, you might adopt a ) and be actively participating in strategic discussions. They should also have a full roadmap that encompasses the needs of all key executives.
Biggest mistakes when hiring your first data person
Although we’ve already touched on this above, it’s worth calling out specifically the biggest mistakes we tend to see.
The first is not setting clear expectations. Either with yourself or with the hire. If all you have is a general sense that “we need more reporting”, it might be a rough ride for all parties. If you’re able to clearly articulate why you’re hiring for this role and what you hope to see in the first 30/60/90 days, all parties will be much better off.
The second mistake is not providing the proper support. We touched on this in detail above, so we won’t linger on this too much here, but not building in the monetary or resource budget to provide them the tools they need and the time to react to their plans or suggestions will render this first hire completely ineffective. Make sure you’re ready to provide more than just their salary.
The third mistake is hiring someone who is very specialized in a certain department. For example you may see someone with extensive experience as both an IC and a leader in “Marketing analytics”, but unfortunately, if this is the individual’s only experience, they may have a really difficult time working across functions and will likely default back to what they’re most comfortable with: marketing. This is great for your marketing department, but they’re surely not the only ones in need of data support.
Finally, the most common mistake is thinking this person just needs to write some SQL, and hiring someone with very limited experience. Without proper mentorship, they will likely get stuck in the technology, miss the forest from the trees, and ultimately end up costing you much more in both strategic direction and tech debt than you might expect.
Your first hire should be deeply technical with experience providing strategic guidance to executives
If you’ve outgrown your pre-canned product and marketing analytics platforms, and you’re starting to wonder if there are insights in your data that could help drive the company strategy, you’re ready to start thinking about building out a data team. But before you do so, make sure you’re ready to provide the financial support they will need along with a willingness to experiment and adjust the plans based on their feedback.
You’ll want an individual with deep SQL and Python experience, who has ideally led and grown data teams in previous startups. This person should be someone that you expect to turn towards for strategic advice, but they’re going to be on their own initially so they should be self-sufficient. Make sure you don’t hire too junior or too much of a specialist.
It may feel daunting to find someone who fits the bill, but they’re out there, and once you find the right person, they will be a force multiplier on your business. Data can truly deliver competitive insights, and conversely, it can be a money pit, so it’s worth taking the time and waiting for the right moment to find the right person. Ultimately, you’ll need to consider the unique nature of your business and team and weigh your priorities to determine which traits are the most important for this role.
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