Four People You Need To Hire In Order To Build An Ideal Data Team.

Before we jump into the specific positions that you need to hire for your data team, we need to ask ourselves a specific question. And that question is what criteria does my data team need to fill?

Want to watch the video version of this article instead? What it here on YouTube:

The reason we ask this question is so that we have a way to measure the success of our team, as well as ensure that both customers and the organization uses the analytics solutions that the team comes up. The criteria that we use to measure our data team is the following:

1. Data Needs To Be Unified.

This means that data needs to be brought together from different sources and loaded into a single place that our engineers and our analysts can work with.

2. Data Needs To Be Accurate.

If data isn’t accurate, then no one’s going to trust any of the solutions that your team comes up with. So when data is loaded and transformed, we need to make sure that everything is done accurately and precisely.

3. Data Needs To Be Fresh.

If your data isn’t up to date and it’s months, quarters, or years behind, then people aren’t going to be interested in the analysis that you provide. That’s why we need to make sure that it’s as fresh as possible, whether that means hourly or even a little more frequently.

4. Data Needs To Be Easily Accessible.

That means that both your analysts need to be able to access the correct data sources in order to do their analysis. And then the business also needs to have access to the solutions that your teams provides so that they can ask questions that maybe aren’t explicitly answered with the dashboards that you build.

Any ad hoc querying tools are critical to have for the business so that they can get answers they need without having to hound you. Now let’s jump in to the roles that we need to hire for our data. The first person that you need to hire for your data team is the software developer. Now, for some people, this might be surprising because the software developer usually isn’t considered part of the data team at all.

But in reality, that software developer is the one who brings all the data into your systems in the first. And if that’s done incorrectly or incompletely, you’re going to be having a lot of issues down the line and your data engineer and data analysts won’t be able to produce any useful results. So the software developer is a very critical part of the team.

I like to call them the integrator because they can bring all of your tools together. They can check all the APIs that can make data, start flowing from tools that maybe you thought you couldn’t collect from in the first place. So they are your first hire for your data team. And one of the most important parts.

Now, the second role that we need to hire for is the data engineer. I know I said that the software developer is one of the most important parts of the team, but in reality, each and every role is just as important in achieving success on your data projects. Now, the data engineer I like to call the master composer.

And the reason for this is because there’s a ton of moving parts that they need to make a symphony out of. They bring together data from all the sources that the software developer configured, and they make sure that they’re loaded and transformed in a way that the data analyst can. The data engineer needs to set up a pipeline that allows data to be fresh and allows data to be structured in a way that the data analyst doesn’t have to spend a lot of time creating complex data models in the tool that they’re building analysis in.

The last thing that we want is for a data analyst to be compensating for issues that are. Earlier on in the pipeline, we want to allow them to be working with the business and with their analysis tool in order to be building useful insights for customers and the organization. Now, the third person that we need to hire for is something you probably could have already guessed because I mentioned it while I was talking about the data engineer and that is the data analyst.

The data analyst is a translator and an artist. They need to create compelling and engaging. Dashboards or visualizations or whatever they’re doing so that the business or the customer is engaged with the analytics that they’re using. It needs to be easy to use and needs to be easy to extract insights out of.

And they’re also a translator. They’re a translator because the business or the customer will say that they need to see or understand a certain data. And what they need to do is take this business requirement and understand exactly what that means from a data structure standpoint. So they need to go back to their schemas.

They need to go back to the data engineer and figure out exactly where they need to look and the calculations that they need to make in order to get those answers to the business or to the. And last but not least the fourth person that we need to have on our data team is the product owner. Now the product owner brings together the entire picture.

They’re the facilitator from the technical side to the business side, they see the entire process from the outset. They’re able to understand the technical side and requirements for the data engineer and for the software developer. And they’re able to work with the business and the data analysts to make sure that the entire project from start to finish is exactly what the vision was at the beginning of the project.

What Does a Data Analyst Career Path Look Like?

Things change throughout projects. So this product owner needs to be adaptable to what the business wants and to what the engineers are seeing. They’re the ultimate facilitator. They’re the go between between the software developer that most likely has never talking to the end user without a good product owner, the entire thing falls apart.

You need to make sure that your product owner is a go getter that understands both the technical and the business side of it. With a great product owner, you can make a great product. So make sure to focus a lot on the quality of your hire and placement for this. So now that I’ve walked you through the four different roles that you need to hire for your data team, we’re going to see exactly how these different roles interact in a project and what they’re responsible for.

What are some other team members that you could add on in order to make your data team even more powerful or more versatile and the type of projects that they can take on?

These are some of the options that you have. The first is the data scientist and optionally, a machine learning or an artificial intelligence specialty. The data scientists can go deeper into the weeds than the data analysts. When it comes to complex analysis, the data scientists is often confused with the data analyst.

But in reality, it’s a different role. It goes deeper into the weeds and deeper into the statistics. Then the analyst typically does more general work. But when you need a specialist, who’s going to dive into things like machine learning, artificial intelligence, or statistics? You need to go with an expert. And that’s when the data scientist is the right hire.

Lastly, is the systems architect. The systems architect was already mentioned in our diagram has sitting next to the product owner on the outside of that. Now the systems architect is a very technical person who can see how different systems and different system components interact. There’ll be able to set up your data projects so that they’re both resilient and scalable.

They’ll be able to work in the weeds with the engineers, the data engineers, the data analysts, and the developers when needed. They can provide the exact technical guidance that you need in order to bring a project across the fleet. I hope that you now know the components you need in order to build a successful data team for your company.