In the history of misconceived job titles and roles, these titles have largely been misunderstood. This can be excused in other organisations but things are quite different in technology.
With big data on the rise, there’s a need to clearly define both roles and understand them distinctly.
Let’s take a quick dive into what both roles have to offer.
A Data Scientist
The first basic fact you need to understand is that a data scientist is focused on finding new insights, from the data that was cleaned and prepared by a data engineer.
Speaking of academic background, a data scientist may have a degree in statistics, mathematics and sometimes physics. This is how they develop advanced analytical skills which aid the discovery, understanding, and communication of data patterns.
Through the process of analytics, a data scientist may develop new algorithms and engage in data visualization and on a rather advanced level, they create machine learning models and a form of artificial intelligence.
A data scientist has a much more direct impact when it comes to scaling up ROI for businesses. Their role requires them to understand the business field enough to create valuable insights, guaranteed to influence vital business decisions.
In simple terms, a data scientist must be able to break down his findings, enough for a non-data scientist to understand. He has to communicate complex results verbally and visually in the most seamless way possible.
It should be noted that a lot of data scientists have programming skills, but it was learned out of a need to accomplish a more complicated analysis.
Data engineers actually have wider programming knowledge, while data scientists are best at analytics.
A Data Engineer
Somewhat different from the background of a data scientist, they usually have a background in software engineering They have in-depth experience with programming languages such as Java, Scala, Python and scripting tools.
A data engineer deals with raw data that contains errors made by humans, instruments or machines. They use their engineering skills to establish software solutions for big data by creating data pipelines.
Furthermore, data engineers focus on taking data from a rather wide range of systems, in structured and unstructured formats – using their programming, integration, architecture, and systems skills to clean all the data and then put it into a format and system that data scientists can analyse.
They deal with a good number of big data technologies and are saddled with the responsibility of understanding and choosing the appropriate tools for the job. Due to certain company preference, the tools of a data engineer may depend on how the role is perceived in the company context.
Regardless of this fact, data engineers often work with tools such as SAP, Sqoop, Oracle, Cassandra, MySQL, Redis, Hadoop, Riak, PostgreSQL, Linux, MongoDB, neo4j, and Hive.
What they have in common
The overlapping skills of both roles aren’t far-fetched, they are quite known to complement each other.
This is because where the data scientist might be having a difficult time scaling, the engineer, on the other hand, finds it easy – an obvious case of vice-versa.
- A data scientist’s skills on analysis are more advanced than the analytical skills of a data engineer. The latter can carry out basic analysis but can hardly execute the advanced analysis a data scientist is trained to do.
- The programming skills of a data scientist are at the peripheral phase, while the engineer is natural.
- Both roles use big data, but differently. A team that allows a data scientist create data pipelines will be heading for a major pitfall. Creating data pipelines is the job of a data engineer while the data scientist creates advanced data products which translate to business solutions.
Combining those roles
While the roles of data scientist and a data engineer can sometimes be interwoven, there’s also an urgent need for specialisation.
The case of a data engineer doing the data scientist’s job might seem trivial. However, mixing these roles can be detrimental to the success of your organisation. No doubt, the overlap is substantial, it is still clear that their roles are distinct and should be treated as such in every organisation.
What questions are you asking candidates who applied for the role of a data scientist?
Are you looking for skills of a data scientist in a data engineer?
Do you require the expertise of a professional analyst from a data engineer?
The combination is quite possible, you can employ one person to do both, and this could be to save cost or any other reason. What cannot be guaranteed is the effective execution of both roles.
Once you begin to expect a scientist to build data pipelines and the engineer to analyse, the whole team becomes confused and in the long run, you waste time and lose funds.
There’s no reason to combine the roles, they simply complement each other.
Read also: 3 Unexpected benefits of working in tech