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How to build a data team

What's the difference between a data engineer, data scientist and data analyst? An explainer.

Making sense of the data. (Photo by Stephen Dawson on Unsplash)

Written by Technically Media CEO Chris Wink,’s Culture Builder newsletter features tips on growing powerful teams and dynamic workplaces. Below is the latest edition we published. Sign up here to get the next one this Friday.

Software without data is like a balloon without air: deflated.

It’s increasingly true across sectors that structured, actionable insights from real-world results power decision making. Small startups, big corporations and even social service nonprofits can use the past to predict the future. This has meant data-related jobs are another fast-growing portion of the American economy.

Nearly 60,000 data scientists and related professionals are employed in the United States, and another 30,000 people are in the related “Computer and Information Research Scientists” category defined by the U.S. Bureau of Labor Statistics. Those are smaller categories than, say, software development, but data science roles are expected to grow at least as fast. More to the point, comfort working with and analyzing data is fast becoming a prerequisite for most any professional role. When assorted titles are included, IBM puts the size of the data field in the hundreds of thousands of employees.

how to build a data team image

The components of a data team. (Graphic by

This surge has caused confusion in title usage and team building. Consider three generally accepted cornerstones of a data team:

  • Data engineering builds infrastructure to aggregate data.
  • Data science develops methods to gain useful and accurate insights from that infrastructure.
  • Data analysis (business intelligence) applies data insights to market trends to make recommendations.

For smaller and newer teams, many of these responsibilities are shared or split. A marketing director or product manager might establish early data-gathering methodologies, gather insights and give recommendations. For the long term, it helps to see these as distinct functions.

Funny thing that some leaders who are new to building out data teams misorder the hiring, says Nicholas Dela Fuente, an instructor and the creator of the data engineering curriculum at The Data Incubator, which was acquired by The Pragmatic Institute in 2019.

“A lot of companies that are looking for a data scientist are really looking for a data engineer in disguise,” Dela Fuente said.

Why? Well, branding, for one. As far back as 2012, Harvard Business Review called data scientist “The Sexiest Job of the 21st Century.” This year, Glassdoor once again ranked data scientist as one of its “50 best jobs in America.” Data analyst also made the list, though far lower, and data engineering was nowhere to be found.

“Data engineering doesn’t get much attention as data scientists, but data scientists need the data engineers first,” he said.

If you don’t yet have a data team, start with the end in mind: What kind of data do you have and what specifically do you hope to get from that data? Is it isolated to a single team or does it have implications for your entire organization?

If you already have a data team, consider its performance: How does information flow? Is this team responsive to outside requests or does it set direction? If data engineers want to improve data collection, data scientists want to improve their models and data analysts wants to make recommendations on that data, which behavior sets the timeline?

“Data is a fundamental part of business today,” Dela Fuente said. “This is something everyone needs to figure out.”

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