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Data Visualization Engineer

Data Visualization Engineers, also known as Business Intelligence Analysts, are specialists in Descriptive Analytics. Employing both analytics and strong design skills, they create color-coded charts and graphs to communicate data findings in a visual way to help facilitate a better understanding of what the data means.

What is a Data Visualization Engineer?

Specialists in Descriptive Analytics, Data Visualization Engineers, also known as Business Intelligence Analysts, create color-coded charts and graphs to communicate data findings in a visual way to help facilitate a better understanding of what the data means. They convert relational, spatial and comparative data sets into visual representations, helping to demonstrate growth, loss or stagnation in the data analyzed, such as time, demographics and more. 

Skills That Set Them Apart

Data Visualization Engineers present numbers, facts and statistics in an image-rich way that help illustrate what the information means. That requires strong design skills in addition to analytics, as they both show the viewers which points are the most valuable, and which points can be filtered out.  In fact, both high and low values, when compared together, may be very important in delivering the desired relational information.

The Educational Foundation That Sets the Stage

Like Data Analysts, Data Visualization Engineers usually attain their undergraduate degrees in subjects that rely heavily on statistics and analysis, including Math, Statistics, Computer Science, Business Management, Finance or Economics. 

Data Visualization Engineers also need a solid understanding of user interface (UI) development so that they’re able to create defined data visualization elements, in addition to the application and data services required to apply those elements to the specific query. This is why many of them pursue additional skills to help them specialize in their chosen field.

Specialized Training

Data Visualization Engineers focus on the design and development of reporting solutions that best convey the results to their clients. They help identify patterns, trends and changes in the data that may be otherwise hidden or not readily apparent by stand-alone numbers and text.  

That’s why they walk the line between the technical and the creative. Technically, they must be able to understand what the data reveals in comparison to the client’s goals before they’re able to create a visual representation of it; and in order to translate the facts and figures into a valuable insight, a creative focus becomes crucial. For example, a simple graph may show peaks and valleys, but it may not highlight some of the less dramatic nuances. Those finer details may be essential to understanding how the highs and lows develop, and an important focus of the report.

Creatively, Data Visualization Engineers need to know how their clients will best respond to the visual analysis. They must be able to interpret and express what the data means, know what colors and shapes to use, and be able to compile the information in such a way that viewers instinctively can interpret what it all means when compiled.


Python  can help Data Visualization Engineers program and transform data that can communicate the results of the query set.


SAS remains in use and continues to see strong support overall within the various data analysis fields.


SQL (structured query language) is the foundation of complex queries because most big data systems use it, along with additional proprietary extensions for more customized use. Even so, the standard SQL commands such as "Select," "Insert," "Update," "Delete," "Create" and "Drop" can still be used to accomplish most tasks – a universal language to master.


Big data calls for big scaling capabilities, and so Data Visualization Engineers should be familiar with NoSQL software such as MongoDB or HBase. These systems work quickly with large volumes of data and can scale accordingly for a more customized approach.

Big Data Computation Networks 

Apache Hadoop, Hive and Pig are not essential, but can certainly help round out Data Visualization Engineers understanding of how to gather and select the right data for the query at hand. 

The bigger the data, the slower the process, so speed is key. Apache Spark is also popular because it’s faster than Hadoop – a boon when running extremely complex algorithms. Familiarity with cloud-based tools can also be a great assistance. Amazon S3 is one of the more popular ones.

Data Visualization Tools

Descriptive Analytics requires Data Visualization Engineers to communicate their findings in an illustrative way; in order to do so, they need to be familiar with the wide assortment of tools that are at their disposal. 

D3.js is at the top of the list for Data Visualization Engineers, because it allows them to showcase data findings from HTML, SVG, and CSS and it provides all of the selected browser functionality without having to adhere to its framework. D3’s data-driven approach combines with powerful visualization components, allowing Data Visualization Engineers to bind arbitrary data to a Document Object Model (DOM), and then apply the indicated data transformations to the document.

Tableau is an essential software package that can present the data and showcase the derived insights. It provides a wide array of tools that allow Data Analysts to drill down further and see the results in a variety of visual formats.  

Data Visualization Commercial


Tableau is a Business Intelligence tool created to help anyone see and understand their data. Connect to almost any database, drag and drop to create...


ggplot2 allows Data Analysts to plot trends on a graph with unique color-coding to help distinguish  between key points. The findings can then be processed directly as a PDF or object that can be easily disseminated to shareholders.

FusionCharts is a JavaScript-based formatting software that charts both web and mobile platform data into three-dimensional graphs. According to New Gen Apps, more than 80% of Fortune 500 companies use it. 

Illustrator shouldn’t be overlooked, either, as it can be a great assist for Data Visualization Engineers. It’s a vector-based, graphic software that is used to develop images. Its versatility is what makes it so valuable across many business applications.

Typical Data Scientist Compensation

What salary can a Data Visualization Engineer expect? Like any position, it depends on the level of experience, the amount of additional training and certifications attained and the demonstrated ability to work well with others. According to the experts, Data Visualization Engineers make:

What to Expect from a Xperra Data Visualization Engineer

Xperra Data Visualization Engineers communicate the data query insights via color and graphs that are delivered in reports and dashboards, helping you understand trends and make educated projections on future business endeavors. Our Data Visualization Engineers can help you make key observations about your customers and their wants, and provide you with lead generation and customer retention opportunities you never thought were possible.


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