What is the Difference Between a Data Scientist and a Data Engineer?
The world of data can often feel a bit like a black box to outsiders or newcomers. A big focus of ours at Data Mettle is making leveraging data for your organisation accessible to as many people as possible. One of the first obstacles you might run across is basic terminology, roles and expertise.
One of the more common sources of confusion is what the difference is between the titles and roles of data scientists versus data engineers. While in the same bucket, these are very different roles, and knowing the difference is critical when beginning to make your first hires for a data team, or when you kick off a data project.
The most basic way to address the difference in these two roles is this: data engineers ensure the data is readily available for the data scientists to use to create answers to organisational questions.
The critical role of the data engineer is to ensure that data is readily available for the data scientists, and other analysts in the organisation. They create systems and databases to collect raw data from multiple sources and ensuring it is usable.
Using marketing as an example, an organisation might be collecting data about customers in a CRM, from the analytics software, customer surveys, and several other sources. A data engineer might marry this data from across these various products into a single source for the data science team to make use of.
Data scientists, on the other hand, are the ones that analyse and provide answers to organisation questions using the data cleaned and made available to them by the data engineering team.
Using our previous marketing example, a data science team might take the data from the CRM, analytics tools, and surveys and begin to predict what characteristics make someone more likely to convert into a customer or increase lifetime value.
Skills Required for Each
Data engineers generally need experience building applications. They’ll likely have substantial experience in SQL and databases and might use programming languages such as Python, Ruby, C# and others. Generally, their background is software engineering. They may have some knowledge on the statistical side of things, but this is a ‘nice to have’ for this role.
Data scientists will likely have experience with databases and programming languages such as Python; the real differentiation will be their proficiency in statistics, maths, machine learning, deep learning and artificial intelligence. They will also need to stay on the cutting edge of research in these fields.
A good data scientist will also have a deep understanding of the business and organisational problems to be solved by their work. This understanding enables them to translate what the data is telling them into a product, tool or model to solve a business need and create an innovative solution.
What we do at Data Mettle
Our focus here at Data Mettle is on data science, reflected in our world-class team with backgrounds in artificial intelligence, machine learning and advanced mathematics. We usually embark on projects that have a strong emphasis on data science skills.
However, we, of course, understand that most organisations, particularly SMEs or startups, will require expertise in both. As such, almost always we end up doing a bit of both: helping organisations get their data in order so we can build them cutting edge solutions and business-critical tools.