Data Scientist or Data Engineer?

Both Data Engineering and Data Scientist are seen very often in recruitment posts. Let's get a closer look in this 10-minutes article from a job market perspective.

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Whenever you look up data scientist in Google, you can find something like How to Become a Data Scientist? or The Sexiest Job of the 21st Century. It is also easy to search for definitions of data scientist. From wikipedia:

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

In contrast, data engineering is less fancy. We are not even able to find an article for it in wikipedia.

So the question we want to answer is: Do we want to be a Data Engineer or Data Scientist?

Understand the job nature

Data engineers build all the architectures that make data available. It requires understanding on various computer systems.

Data scientists, focus on stuff like mathematics, statistics and modelling. However, do not underestimate the technical skills required by being a data scientist. A data scientist still need to code and to work with engineers. If anyone became a data scientist without knowing software development, trust me, he is super excellent in either mathematics and statistic or deceiving his hiring manager.

Who is hiring?

The world of data is a composition of engineering, statistics and mathematics. Depending on the needs of a company, the data team¹ can have a very big variance in composition. Here I listed out a few kinds of companies that are actively recruiting data related people.

¹ Data team – A general name for Data Science Team, Business Intelligence Team, Data Analytic Team, etc..

Internet service company

Companies that provide internet services have huge amount of data. The service can be a forum, a mobile game, a e-commerce website, etc.. Such data is generated when customers browse around the application.

Example of active openings:

Required engineers > scientists

Data heavy traditional company

Other than internet services, some tradition companies also have huge amount of data to analyst, for example, bank and insurance companies, retail companies from luxury brands to grocery stores.

Example of active openings:

Required engineers > scientists

Tech-heavy company

In the above two examples, data is used to assist the existing business. However, the other kind of company is some tech-heavy company, which sell tech as their main product. They are usually not big companies.

Required scientists > engineers

For now, let’s leave aside the highly academic roles. In these companies, the data team is usually required to handle

1. Data pipeline – Sending data from front-end and back-end into the data warehouse.
2. Reporting – Preparing dashboards from sales to user acquisition to user profiling
3. Forecasting – Using machine learning models to perform prediction on e.g. traffic tends

Summary

Both data scientist and data engineer require understanding in the computation world. To get your first step into the data world, acquiring skills to be a data engineer is a easier way because of a higher demand in junior positions.

After that, you can extend you career path to for example, cloud architect, data scientist, dev-ops developers, which are all known to have nice salary package offers.