Even if you’re on the data team keeping track of all the different roles and their nuances gets confusing—let alone if you’re a non-technical executive who’s supporting or working with the team.
One of the biggest areas of confusion is understanding the differences between data engineer, data scientist and analytics engineer roles.
A data engineer develops and maintains data architecture and pipelines. Essentially, they build the programs that generate data and aim to do so in a way that ensures the output is meaningful for operations and analysis.
Some of their key responsibilities include:
Important skills for data engineers include:
An analytics engineer brings together data sources in a way that makes it possible to drive consolidated insights. They do the work of building systems that can model data in a clean, clear way repeatedly so that everyone can use those systems to answer questions on an ongoing basis. As one analytics engineer at dbt Labs put it, a key part of analytics engineering is that “it allows you to solve hard problems once, then gain benefits from that solution infinitely.”
Some of their key responsibilities include:
Important skills for analytics engineers include:
A data scientist studies large data sets using advanced statistical analysis and machine learning algorithms. In doing so, they identify patterns in data to drive critical business insights, and then typically use those patterns to develop machine learning solutions for more efficient and accurate insights at scale. Critically, they combine this statistics experience with software engineering experience.
Some of their key responsibilities include:
Important skills for a data scientist include:
Even seeing the descriptions of data engineer, data scientist and analytics engineer side-by-side can cause confusion, as there are certainly overlaps in skills and areas of focus across each of these roles. So how does it all fit together?
A data engineer builds programs that generate data, and while they aim for that data to be meaningful, it will still need to be combined with other sources. An analytics engineer brings together those data sources to build systems that allow users to access consolidated insights in an easy-to-access, repeatable way. Finally, a data scientist develops tools to analyze all of that data at scale and identify patterns and trends faster and better than any human could.
Critically, there needs to be a strong relationship between these roles. But too often, it ends up being dysfunctional. Jeff Magnuson, Vice President, Data Platform at Stitch Fix, wrote about this topic several years ago in an article titled Engineers Shouldn’t Write ETL. The crux of his article was that teams shouldn’t have separate “thinkers” and “doers”. Rather, high-functioning data teams need end-to-end ownership of the work they produce, meaning that there shouldn’t be a “throw it over the fence” mentality between these roles.
The result is a high demand for data scientists who have an engineering background and understand things like how to build repeatable processes and the importance of uptime and SLAs. In turn, this approach has an impact on the role of data engineers, who can then work side-by-side with data scientists in an entirely different way. And of course, that cascades to analytics engineers as well.
The truth remains that many organizations define each of these roles differently. It’s difficult to draw a firm line between where one ends and where one begins because they all have similar tasks to some extent. As Josh Laurito concludes: “Everyone writes SQL. Everyone cares about the quality. Everyone evaluates different tables and writes data somewhere, and everyone complains about time zones. Everyone does a lot of the same stuff. So really the way we divide things is where people are in relation to our primary analytical data stores.”
At Squarespace, this means data engineers are responsible for all the work done to create and maintain those stores, analytics engineers are embedded into the functional teams to support decision making, put together narratives around the data, and use that to drive action and decisions, and finally, data scientists sit in the middle, setting up the incentive structures and the metrics to make decisions and guide people.
Of course, it will be slightly different for every organization. And as blurry as the lines are now, each of these roles will only continue to evolve and further shift the dynamics across each of them. But hopefully, this overview helps solve the question of what’s the difference between data engineer vs data scientist vs analytics engineer—for now.
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