Business organizations of all sizes have gone digital and are adopting a broad range of data science and data analytics initiatives. Whether it’s marketing, sales, operations, customer support, accounting, supply-chain management, manufacturing, or any other department, the need for well-governed, high-quality, and up-to-date data that’s ready-made for their analytical requirements has never been higher. Many businesses have attempted to satisfy this requirement with centralized data engineering teams in the past, but these teams have struggled to keep up with the growing demand from all of these different activities while maintaining the timeliness that they anticipate.

This is where the Citizen Data Engineer comes in. They are frequently the most hands-on data-savvy analyst inside analyst teams since they are usually entrenched inside business units outside of IT. While not trained as data engineers, many have acquired SQL and scripting abilities along the road and are able to set up their own rudimentary data pipelines to meet the demands of their organizations. Furthermore, citizen data engineers work for their own group, laser-focused on obtaining the data they want as soon as possible.

As a result, large businesses have little patience for enterprise data technologies that take days or weeks to implement the data pipelines they require, and in the absence of a fit-for-purpose tool, they often go it alone, hand-scripting their own pipelines or putting together a patchwork of point tools to get the job done. Self-service and agility are vital.

Many organizations are still developing citizen data engineers. Most Chief Data Officers and other enterprise-wide data leaders don’t want to slow down the business, so they’re trying to empower citizen data engineers, but they’re having trouble doing so with centrally managed tools that don’t meet their needs for “just in time” pipeline creation and subsequent change management. This has created a “wild west” scenario in which each citizen data engineer selects their own tools to fulfill their own requirements and establishes their own data quality, data recency, data privacy, and other “Data SLAs” for their particular teams.

The Cloud gives the citizen data engineer even more authority making it easier for citizen data engineers to get started with cloud-hosted tools without having to wait for IT or a service provider to provide data integration software for them. Indeed, as the pandemic caused more enterprises to work remotely and migrate their data analyses to the cloud, citizen data engineers have sprung up to set up data pipelines in the cloud during the last year. In order to meet this need, there has been a profusion of point products that promise cloud data pipeline agility and self-service – but fail to assure consistency in data quality and governance across teams, as well as keep up as each team’s varied data demands expand.

Why Citizen Data Engineers?

In today’s environment, having a good data engineering team is critical, and more firms are recognizing this. Without the help of data engineers, most of the work accomplished by data scientists becomes very difficult. 

The amount, velocity, and diversity of data accessible to data scientists grow every day, and data engineers are critical to their success by developing scalable, reliable, and efficient data processing and delivery systems. Unfortunately, there aren’t enough data engineers to match the demand. 

Data analysts often use descriptive analytics to uncover patterns in data from previously occurring events and present their findings in dashboards, static reports, and graphs. Citizen data scientists and engineers,  AI engineers now have strong tools that allow individuals to tackle business challenges with more depth and breadth than their professional data analytics counterparts. 

They can create models that suggest the next best course of action, identify prospects who are most likely to purchase a product, assess which loans are most likely to fail, and more. Citizen data scientists are having such an impact that Gartner believes that the amount of advanced analysis and economic value created by citizen data scientists will have surpassed that of data scientists.

The rise of the citizen data engineer exemplifies a trend in which people who aren’t part of the data engineering team will be in charge of data pipelines and the whole data lifecycle. 

Individuals will begin to take on these new roles, either by choice or necessity, in order to meet the ever-increasing demand for data engineering. Citizen data engineers will fill the gap in the future by helping organizations construct and manage pipelines, automate digital transformation initiatives, and see data-driven projects through to completion.

Scope of Citizen Data Engineers

The data scientist’s function is continually developing, but data remains at its heart. Setting realistic expectations for what you’ll perform as a data scientist is critical, and mastering data engineering tools will undoubtedly prepare you for the real world.

Data is the foundation of data science. The data you feed into your machine learning model is just as important as the model itself. Without sufficient data, a data scientist will be unable to develop a worthwhile product. For data scientists, the right data isn’t always readily accessible. In most circumstances, the data scientist will be responsible for converting the raw data into a usable format. You should be able to perform certain data engineering chores unless you work for a large tech business with distinct teams of data engineers and data scientists. 

Data science is still in its early stages, and most businesses do not have well-defined responsibilities for data engineers and data scientists. As a consequence, a data scientist should be capable of doing some data engineering chores.

Citizen Data engineers, for example, perform a series of activities that includes the steps for gathering data from one or more sources, performing transformations, and loading the data into another source.

If you anticipate working as a data scientist just on running machine learning algorithms with ready-to-use data, you will quickly discover the unpleasant reality. To preprocess the client data, you may need to develop certain SQL stored procedures. It’s also feasible that you get customer information from many distinct places and combine them into a single source which requires SQL knowledge to build effective stored procedures. Many data cleansing and manipulation activities are included if you deal with enormous amounts of data and a citizen data engineer may ease up your work. Spark might be your finest distributed computing ally, a big data analytics engine that can handle a lot of data.  You’ll have no trouble learning Spark if you’re already comfortable with Python and SQL. PySpark, a Python Spark API, may be used to access Spark capabilities. When it comes to dealing with clusters, the cloud is the best option. Although there are other cloud providers, AWS, Azure, and Google Cloud Platform (GCP) are the most popular.

Closing the gap

Many data engineers come from backgrounds in software engineering, database administration, infrastructure, and tools, or other relevant information technology professions. Numerous firms have already started identifying and training internal data engineers to help address the shortage of data engineers, so generating an opportunity for people interested in the discipline, and this trend will definitely increase over the coming years.

With the sustained and substantial increase in data engineering-related professions, there simply will not be enough data engineers to match the expanding need. Citizen data engineers will become critical to the success of data-driven organizations in order to close the gap.

Citizen data engineers often work in a field of business such as marketing, sales, finance, or human resources and have extensive domain knowledge of the business difficulties faced by their department. Armed with robust software, they do in-depth diagnostic analysis and develop machine learning models to augment the job previously performed by a data scientist, mathematician, or statistician.

Citizen data engineers collaborate with data scientists on projects requiring extensive business experience in the majority of artificial intelligence (AI)-driven enterprises. This enables conventionally trained data scientists to concentrate on more sophisticated initiatives that have a broader influence on the corporation as a whole, rather than just on the KPIs of a single department.

DataSwitch’s pioneer DIY toolkit DS Integrate is built to create better and empower existing citizen data engineers. For a live demo, click here.