Lost in data-related job terminology?

Last week, I ran into an article describing 12 different jobs in the data-related world. Far too complex for me. They even managed to refer to a job that was not in their own list…
I am struggling with this regularly, and I am sure I am not alone.

Most articles concerning this theme seem to be made for organisations with +10k employees and more, but what about small, medium and semi-big companies. I am sure they don’t have 12 different job titles in their ‘data department’.

So I started looking for a good description, but so far without real “aha-erlebnis”. I can however refer to some interesting articles (see below).

Data engineer in action … ?

My summary so far, mainly based on the Udacity paper:

  • Data analysts : they analyze data, provide reports and visualizations in order to present the insights the data is revealing. Mostly, a data analyst summarizes the past. They can do data manipulation, and apply statistical techniques to practical problems.
  • Data scientists : ‘comparable’ to data analysts, but he/she can do undirected research and tackle open-ended problems and questions. Their work can uncover new business opportunities or save the organisation money by identifying hidden patterns in data. They use Python and R, and apply practices of advanced math, statistics and ML.
  • Data engineers: data engineers can build a robust, fault-tolerant data pipeline that cleans, transforms and aggregates unorganized and messy data into databases or data sources. So they are responsible for compiling and installing database systems, writing complex queries, scaling, and putting disaster recovery into place.
  • Business analysts : (often overlooked I think) somebody must really understand and know the business and the related data. In the complete data science workflow, this must be the first step. Somebody must be able to define and understand the context, the challenges and the goals, and sketch the use cases for the data jobs.

Of course other descriptions and subdivisions are possible. If you have a good one, just send it to me.

This raised the question: What am I? An existential question…
Given my educational and professional background I guess I am some kind of combination of many of the above. So no in-depth specialist, but one with a broad view on most magical aspects of data combined with real practical experience.

I recently learned a new term to describe this role – one with great future if I should believe the paper. I might describe it in a next blog.

TMWS Info Card:
⁞ Time Well Spent : +/- 2 hours
⁞ Money Well Spent : €0
⁞ Type of learning : articles
⁞ More info : e.g. on Udacity, Datasciencecentral, Computable and many more on the internet…