10
min read

4 Positions You Need On Your Data Team

Looking to build a data team? Don't know which positions to hire for? Take a look at this article!

Having the right personnel in place is the entire differentiator between success and failure. From sports teams to board rooms, assembling the right skill sets and personalities makes the different between winning a championship and being out of business in a few years.

Data teams are no different. Let's take a look at what these positions need to achieve, what they are, and how you can build a championship data team at your business.

What criteria does my data team need to fill?

The reason we ask this question is so that we have a way to measure the success of our team, as well as ensuring that both customers and the organization actually uses the analytics solutions that the team comes up. Why would we want to spend time and money on something that people ultimately don't use?!

The criteria:

1. Data needs to be unified

This means data needs to be brought together from all your different sources and loaded into a single place. This unified data source will be the single place that engineers, analysts, and ultimately the business can work with all the data they'll need.

2. Data needs to be accurate

If data isn't accurate, no one will trust any solutions or insights your team comes up with. Quality checks need to occur each step of the way when data is loaded and transformed. Each step needs to be accurate and precise.

3. Data needs to be fresh

What good are insights that are delayed by 3 months? A modern business needs to be able to adjust to changing conditions today, not next quarter. Otherwise, people won't be interested in the analytics and insights provided. Whether that's data daily, hourly, or a little more frequently, it just can't be weeks or months.

4. Data needs to be easily accessible

We can break this down into two main areas. Correct data sources need to be accessible for analysts so they can do their work without blockers and delays. The business needs to be able to easily access the data solutions your team builds. By making data easily accessible, people can ask ad-hoc questions about data that you might not cover in your dashboards and other data solutions.

All the activities of your data team should be driven towards these criteria. Otherwise the effort is for naught. No one will trust the solutions they provide and therefore no change will occur.

Let's jump into the positions!

The 4 Positions You Need On Your Data Team

1. Software Developer

Yes, yes, you might be surprised at this. But, if we start with the common adage; garbage in, garbage out, it makes more sense. The Software Developer is the one who sets up all of the input for your data systems. They're the integrator who connects all the tools and systems so that data flows into the right places in your data stack.

This position will vary depending on the data inputs that your organization needs, but the overall position description is a Software Developer. They could have specializations in Python, Javascript, web development, etc. They just need to know how to work with code, APIs, and data structures.

And since you don't want garbage in, this is one of the four critical positions you need.

2. Data Engineer

While your Software Developer is developing the inputs of your data stack, they need guidance and a specialist to bring that data together in a usable format. That's why the Data Engineer is the next position needed.

This Data Engineer is like a master composer. They're creating a symphony out of all the moving data and data stack parts. They're making sure that the data is extracted, transformed, and loaded in a way that the rest of the team can utilize. They're also the chief driver of all the criteria that needs to be fulfilled with the data.

Since they're at the beginning of the data funnel, they need to make sure all criteria are met before anyone else touches the output the Data Engineer produces.

One important note is that the Data Engineer needs to have a fundamental understanding of the business, its processes, and its goals. Regardless of what you might hear, Data Engineering solutions are opinionated and therefore need to be carefully designed.

3. Data Analyst

The data analyst is a translator and an artist. They need to create compelling and engaging insights so that the business or the customer is engaged and deriving value from those insights. They need to translate what the business is asking for and needs in order to drive change using data. They also need to translate what the data is saying in a way that the business can make decisions quickly and accurately. 

A critical thing to mention is that the Data Analyst and the Data Engineer must work hand-in-hand.

The Data Analyst should not be doing the bulk of transformation work around the data. This should be handled by the Data Engineer and therefore the two need to be in sync about the needs for the analytics of the business. Some small transformation work will always be needed, but the Data Analyst should not be spending the majority of their time doing transformation work.

4. Product Owner 

Last but not least. The Product Owner (which could also be a Technical PM) puts together the whole vision and execution of the data team. From the technical coordination to the business communication and understanding, the Product Owner is the facilitator.

They translate, challenge, and unify the business needs with the day-to-day work of the Software Developer, Data Engineer, and Data Analyst. They react to changing business needs and arising technical challenges the data team is facing. They're the ultimate facilitator! 

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 PO, you can make a great product. So make sure to focus a lot on the quality of your hire and placement for this.

Wrapping Up

Recruiting competent personnel that can execute complex projects together is no small task. But outlining the positions and how they will work together is a great starting point. The positions we listed will make up a team that can bring your organization from zero to one with your data and analytics capabilities.

As your needs grow more complex, your team will need to change as well. Data Scientists, Machine Learning Engineers, Artificial Intelligence Engineers, and Data Architects can all be added onto your team in order to tackle harder problems. The questions is; will you build this team yourself or hire an external company that can bring a fully-prepared team to the table instantly?

Dan Saavedra

Owner

Dan is an expert in automation and data visualization. He approaches problems as interconnected networks, and likes to extract insights by connecting dots between obscure topics.