Growth Engineer vs Data Scientist
There is lots of talk right now about data-driven marketing. From big data to fast data, data engineering to growth, the buzzwords are piling up…and it can be confusing. Solve Growth sits at the nexus of it all so we wanted to add some clarity. The following post will help you better understand the position of Growth Engineer and how it differs from that of a traditional Data Scientist so you can determine what is the best fit for your company’s needs.
What Is A Growth Engineer?
Growth Engineering is best understood by breaking down each word. In short, any role focused purely on acquiring users/customers is considered “growth”. An engineer is best defined as a person who designs and builds (a machine or structure). Combining the two simply means that a growth engineer is someone that designs and builds systems for acquiring more users or customers.
But, while simple in definition, the role isn’t quite that easy in execution, as we’ll see.
What Skills Are Needed To Be A Growth Engineer?
In short, any growth engineer worth their weight is going to need to understand both data (capture, measurement, analysis) AND marketing strategy.
A marketing team needs someone who can interpret the data they are gathering and make actionable recommendations quickly. The second part is critical. Fast Data is not only about the recency of the data but also about making it actionable in a shorter period of time. There will be times when the inbound data is actionable independently. On other occasions, the data will require other data sources to validate a hypothesis.
In short, your growth engineer needs to be able to combine separate data sources and draw conclusions in a statistically accurate manner. That requires someone who is familiar with APIs, databases and someone who knows statistics.
In these situations, you will need a growth engineer that is capable of incorporating external data sources in a short period of time. What’s the best source of affordable data nowadays? It comes from APIs. You can make a call to an API endpoint nowadays and either get data for free or on a cost per call basis. Both options are significantly cheaper and faster than purchasing the data from a vendor or through conducting an extensive research campaign.
So, the skill that is needed from your growth engineers is an understanding of programming, even a basic one. You’ll need a team growth engineer that can write code at a level sufficient enough to retrieve information from APIs.
Similarities & Differences With A Data Scientist
In truth, a Growth Engineer is many ways very similar to a Data Scientist with domain expertise in marketing. There are specific areas of knowledge commonly expected out of a Data Scientist that are not required for a Growth Engineer: A) Machine learning and algorithms B) Familiarity with technologies and frameworks typically associated with Big Data.
Are there any expected areas of familiarity expected out of a Growth Engineer that might not be found in a Data Scientist? Yes. The biggest difference is that a Groth Engineer will be A) Capable of translating insights from data analysis into actionable marketing strategy B) Familiar with current web frameworks and languages.
While there are many commonalities, there are also nuanced differences between a Growth Engineer (with Fast Data) and a Data Scientist (with Big Data). The biggest of which might not relate to technical capacity at all. Namely, the speed required in one versus the other is drastically different. I assume this will lead to different personalities being more drawn towards one versus the other.
We hope this post helps clear up some common misconceptions between Growth Engineers and Data Scientists.