You want to hire your company’s first data team member. But you’re not 100% sure on what you should be looking for.
Let’s run through some things to think about.
What are your goals?
Data can be a big bucket. Here are some questions for you and your team to think about to shape the responsibilities and profile of the person you’re looking for.
Do you plan to build out a team? Do you want this person to lead that team?
If you hire a people manager, they’ll expect to hire people early in their tenure. On the other side, if you hire an individual contributor, you should know whether or not they want to be managing people in the future.
What types of data work is needed now and what will they be doing?
Are you embedding analytics into your product? Do you need help establishing internal metrics and KPIs? Are you trying to automate manual processes? Are you struggling to get experimentation and A/B testing off the ground?
These are often different archetypes of data practitioners.
How intensive is data to your operations?
Every company wants to make the right hire the first time. But if this person is doing any customer-facing work or something relating to logistics/inventory, a miss could be even more damaging.
What is your alternative to a data person?
Do you have anyone on the team now who is handling data requests in the meantime? Can you hire consultants? You need to be able to state why this is an urgent need to your company at this time.
Data individual contributor archetypes
Let’s levelset on the types of individual contributor roles in data.
Data scientist
The buzziest job title of the 2010s, startups notoriously hired data scientists, made them do data analyst and data engineer work, then realized they made a mistake and moved on.
Data scientists are very helpful for experimentation and predictive modeling, mainly working in Python.
Data analyst
Data analyst responsibilities vary tremendously based on industry and company. Let’s focus here on what you typically see at startups: analysts use SQL (and sometimes Python) and data visualization tools to partner with the business and help stakeholders make decisions. Technical skills can be all over the map.
Data engineer
Data engineers maintain your data infrastructure. This can include your data warehouse, data pipelines, and DevOps for other members of the data org. They will work mostly in Python and sometimes in SQL.
Analytics engineer
The newest title of the bunch, analytics engineers are the bridge between data engineers and data analysts. They work with data inside your warehouse and transform it to be analysis- and dashboard-ready. Note that analytics engineering is a task that analysts can and should do, but you don’t want them spending too much of their time doing it.
Michael Kaminsky’s formative post on Locally Optimistic is still relevant many years later. However, I have started to see some title inflation in the field — some companies are retitling “data analyst” as “analytics engineer” and creeping some dashboarding and analysis work into it.
I’m also seeing some hiring managers prioritizing analytics engineers early on because they want someone to manage the plumbing for agentic AI analysis rather than hire an analyst.
Which one do you want first?
Unless you really need data science immediately (and often when you think you do, you don’t), you probably should not start with a data scientist. Similarly, if your data is coming from your application database and/or common tools like Salesforce or Stripe, I’d recommend buying a data pipeline from Fivetran rather than hiring a data engineer to build it, when you’re just starting out.
That leaves data analyst and analytics engineer. Choosing between these two will hinge on how commercial you want this person to be.
If you go with the analyst, you probably still want someone on the more technical side of the spectrum. If you want someone to be a strategic partner in defining metrics and analyzing data, I’d default in this direction.
The same thing goes for the analytics engineer. This person would be stronger technically but they’ll need more commercial skills than the average analytics engineer on a bigger team. This might be a better fit if you’re planning to fast-follow with hiring data analysts later, or want to go all-in on agentic AI for analysis.
Seniority
How senior you should hire is going to depend on factors like your goals, budget, and company size.
If you’re hiring just one person, you will need them to be scrappy and hands-on with doing some (or all) of the work themselves. Each candidate has their own preferences, but you are less likely to see Chief Data Officer and VP of Data candidates want to do this. Plenty of Director of Data candidates will also prefer to manage “strategy” and hiring.
So if you do want someone who’s a proven people manager, you’re likely looking at someone at director or manager level. They will be more expensive than an individual contributor, though, and they’re going to want to build a team around them.
If you have a smaller headcount and expect a lean team for the immediate future, a senior or staff-level individual contributor could be the way to go. This can be win-win — you get someone motivated to develop and deliver results, and they get a chance to learn at a much faster pace.
Putting it all together
We’ve talked here about some of the things to think about and look for when making your company’s first data hire. There’s no one-size-fits-all answer, but definitely some pitfalls and traps to avoid. Be very intentional about how this connects to your goals and what you need to learn to be able to make an informed decision.
Have more questions or want to chat further? Drop me a line at jacob@riverboat.ai.