In recent years, along with the growth of data being generated, organizations have been investing in order to leverage that data.
Hence, there is a need for data-savvy personnel, who are able to observe, translate and pass on the information about the environments in which the organization operates, should it be a business, technological, financial, or any other environment.
These teams of data professionals are in fact the eyes and optic nerves of the organization.
Since there is no widespread standard for the job titles and descriptions, the titles and functions of professional data workers are often misunderstood.
Some titles are not being associated with their correct function, some titles are congruent with multiple functions and some are just made up.
For example, data architect and data engineer titles can be confused, sometimes titles are a bit different or a mixture of multiple titles, like Analytics, BI engineer, or BI analyst.
This article is an attempt to organize and shed light on the different functions of data personnel in the workplace.
The common data positions:
- Data Architect
In charge of planning the entire ecosystem of the data structures and processes, security, robustness, efficiency, and costliness of the system.
- Data Engineer
In charge of the construction of the system, databases, logging, fail-safe mechanisms, and scheduled processes.
- DBA — Database Administrator
In charge of the maintenance, control, and monitoring of the data storage, security, integrity, performance, and user management.
- BI Developer
In charge of the movement of data in the organization, from the data storage to wherever needed, users, analysts, scientists, finance, sales, and management, assures relevant quality and effective data flow.
- Data Analyst
In charge of descriptive statistics, crafting the data into a story, creating measurements, processing, visualizing what happened and what can be learned from it, giving relevant insights for decision making.
- Data Scientist
In charge of descriptive and inferential statistics, extrapolating the data into predictions, building machine and deep learning models in order to make super-powered calculated guesses about the yet to be known.
Depending on the orientation of the position, the scale of the organization, the initiative of the personnel, and other factors, the functions of these positions may overlap.
yet, the three main functions of any data personnel are:
- Create and maintain a system that moves data from point A to point B.
- Processing and manipulating the data to fit the goal at hand.
- Using the end product for visualization, decision making, or automation.
They vary in scale and complexity according to the position, and different tools and skills apply to each level of scale and complexity, but each position does these three at some point of their work process to a certain degree.