The Emergence of Data Engineer, Data Architect, Data Scientist, Citizen Data Scientists and Analytics Translators

The Emergence of Data Engineers, Data Architects, Data Scientists, Citizen Data Scientists and Analytics Translators

Within the next two decades, it is estimated according to the Economist magazine that 47% of all employment will be taken by machines. This will be the greatest challenge our societies, on a worldwide scale, will have to face. This state of affair will be mostly caused by the rapid evolution in Artificial Intelligence (AI) and automation of white collars jobs.


Our societies must today prepare themselves to this challenge and a new model must be worked out to ensure that we do not endure major social unrest in the years to come as for example it is highly likely that a large number of today's babies will never work in their lifetime as we have in ours. Of course, this prediction may not be accepted by many people today who may feel that we will be able to create new jobs, etc... However, looking back at what happened to blue-collar workers when automation started to replace them in factories, may give us some clues about the future of white-collar workers. It was then perceived that technological advancement would not lead to blue-collar job unemployment, but rather shifts in the types of work performed. Proponents of this idea viewed coding as a replacement for blue-collar jobs and suggest that more coders will be needed in a technologically advancing world, where these new white-collar IT jobs could be filled by displaced blue-collar workers. The success of this approach was never been achieved on a big scale. Regarding the white-collar workers’ future, this may be even worse as there will be no new horizon for white collars workers to go to as they will be terminally affected by this AI and automation evolution.


In the new society model, receiving money will most likely have to be replaced in one way or another by universal credits given by governments to their citizens with extra compensation given for jobs undertaken by people. These jobs may be very much community based for many such as, for example, artistic or caring. However, for the more industrially or technologically minded future men and women, the choice of working alongside AI will become the norm for the remaining jobs…. Even politicians will have to embrace this change of direction in their jobs, hopefully for the better of humankind…. But this goes into a different story.


The emergence of AI specific workers should be taken seriously by our education bodies and ensure that the right type of workers come out of the schooling system. Looking at the past, we can clearly see how the education bodies have failed to meet our societies career needs. In this article, we want to look at the emergence in importance of jobs that will become more and more needed by tomorrow public and private sectors. We will therefore look at the following future proof jobs that children should be aware of for their future careers:
· Data Engineer
· Data Architect
· Data Scientist
· Citizen Data Scientist
· Analytics Translator


Data Engineers are the corner stone for these jobs. Data engineering role focuses on the end application of collecting and analysing data. They create and maintain the analytics infrastructure which is responsible for enabling the different operations in the data industry. Architectures like processing systems and databases are developed, constructed, maintained and tested by them. Also, they must closely monitor the new trends in the data industry and develop new algorithms that make the raw available data more useful in a business enterprise. This also entails improving the quality and quantity of data by improving and leveraging the analytics system of data that is then made available to the Data Scientists. Therefore, data engineering is basically an IT role that benefits from them having acquired technical skills like SQL database design, and different programming languages.
Data Architect are responsible for the designing, creating, deploying and managing an organisation's data architecture. They define how the data will be stored, consumed, integrated and managed by different data entities and IT systems, as well as any applications using or processing that data in some way. They, therefore, provide a standard common business vocabulary, express strategic data requirements, outline high-level integrated designs to meet these requirements and align with enterprise strategy and related business architecture.
According to the Open Group Architecture Framework (TOGAF), a Data Architect is expected to set data architecture principles, create models of data that enable the implementation of the intended business architecture, create diagrams showing key data entities, and create an inventory of the data needed to implement the architecture vision. The skills required for this job include knowledge in RDBMS, user interface and query software, backup and archival software, Agile methodology and ERP implementation, predictive modelling, NLP and text analysis, data modelling tools, data mining, UML, ETL, Python, C/C++, java, Perl, XML, UNIX, Windows, machine learning and data visualisation. Ideally Data Architect will generally have a bachelor’s degree in computer science, computer engineering or a related field. Coursework should include coverage of data management, programming, big data developments, systems analysis and technology architectures. For more data architecture senior positions, a master’s degree is usually preferred.


Data Scientists’ responsibilities include gathering and analysing data and using various types of analytics and reporting tools to detect patterns, trends and relationships in data sets. Data Scientists typically work in teams to mine big data for information that can be used to predict customer behaviour and identify business risks and opportunities. These professionals are therefore tasked with developing statistical learning models for data analysis and must have experience using statistical tools, as well as the ability to create and assess complex predictive models. Hard skills required for the job include data mining, machine learning and the ability to integrate structured and unstructured data. Experience with statistical research techniques, such as modelling, clustering and segmentation, is also often necessary. Data science requires knowledge of several big data platforms and tools and programming languages that include SQL, Python, Scala and Perl, as well as statistical computing languages such as R. They need to have the ability to push against corporate expectation, thus having leadership qualities as they define best practices and drive the enterprise culture toward a data-driven decisions approach, establish goals and KPIs. Data Scientists must also be able to step back and identify data biases; where the data came from, who the data sources are and what is not included in the data. They would generally have degrees and experience in advanced maths, computer science and business disciplines. Due to a shortage, in 2019, of such candidates, employers had to look at filling the gap by employing researchers from the hard sciences, such as mathematics and physics, that can be a good fit. The reason is that in these disciplines, statistical analysis of large data sets is quite common.


Citizen Data Scientists are individuals who contribute to the research of a complex data initiative but who does not have a formal educational background in data analytics or business intelligence. A Citizen Data Scientist can contribute valuable research to a topic, whether through performing time-consuming data checks, meticulous data preparation or by discovering anomalies in the data they are handling. While a citizen Data Scientist may not perform a formal Data Scientist job function, they still play a vital role and may participate in breakthrough discoveries. When Citizen Data Scientists can master the tools used by the experts, they act as valuable members of an organisation. Citizen Data Scientists do not therefore replace Data Scientists but are intended to collaborate with them to accomplish more work in shorter time-frames. Currently, the role of a Citizen Data Scientist has become more important for organisations to incorporate as there is a shortage of trained Data Scientists. Therefore, data science jobs can be filled by employees with various backgrounds that know how to use big data tools and create data models. By using skills across teams or training employees in new areas, organisations can save money, operate more efficiently and make better use of data. Citizen Data Scientists should become familiar with skills like machine learning, business analytics, statistics and coding in various programming languages. It is difficult today to say if Citizen Data will still be required in the long-term future as there may well be enough Data Scientists in the marketplace. We can however expect a need for them for at least a few years.


Analytics Translators, like Citizen Data Scientists, do not require specialised data analytics or IT training. However, analytics translators start the process that is carried out by a Data Scientist or Citizen Data Scientist. They use tools and business intelligence to help identify patterns, trends, problems and potential opportunities are cross-functional business activities. They initiate the initial research that is then passed over to Data Scientists to dive further into the nuances, produce reports and make decisions. Like the Citizen Data Scientists, they should become familiar with skills like machine learning, business analytics, statistics and coding in various programming languages.


The need for future jobs to be AI related is so critical that as parents, as educators, as business leaders and as politicians, we have to take on the challenge and ensure that our children are given the best opportunities and directions to ensure that they are well equipped in tomorrow’s world. Never in the history of mankind, have we been faced with such a great challenge. According to the United Nation’s statistics, by 2030 the world population will reach 8.6 billion inhabitants… with 80% world unemployment. However, it would be naive to think that the challenge is a decade away…. It is already with us today!