Data Engineering: What is it and Why is it Essential in the Era of Big Data?

Data Engineering: What Is It and Why Is It Essential in the Era of Big Data?

We’ve written before about the buzz around data science, which is broadly defined as the extraction of knowledge and actionable insight from raw data. Given the explosion of data being generated by users worldwide and the development of new machine learning (ML) and artificial intelligence (AI) programs, it’s no secret that data science is now considered to be extremely valuable to governments, businesses and other organizations. And yet, an estimated 87% of data science projects never even make it to production, and even fewer add any tangible value. Why?

In truth, there are a number of possible factors, but one important reason is that organizations don’t have the infrastructure in place to transform vast swathes of raw data into a format that can be used by data scientists to extract value. And that is when data engineers are needed.

Data engineering is difficult to define precisely, but it is fundamentally about designing and building the infrastructure needed to collect, clean and format data so that it is accessible and useful for end-users (usually data scientists). It is sometimes considered an extension of software engineering focused on data, or as a cousin of data science. Another way to look at it is as a crucial step in the hierarchy of data science needs: without the architecture built by engineers, the analysts and scientists won’t be able to develop effective models to draw insights from raw data. And that risks leaving organizations unable to leverage one of their most valuable resources. 

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The Evolution of Data Engineering

Though the basic function has existed for many years, the title of ‘data engineer’ only really hit the mainstream over the last decade. This coincided with the growth of data-driven applications like Facebook - the surge of real-time user data created a need for new tools and frameworks to store and process the valuable business information contained within. Data engineering took off, and hasn’t looked back since. 

Now, in the era of Big Data, it’s one of the most sought after titles. Indeed, DICE’s 2020 Tech Job Report highlighted ‘data engineer’ as the fastest growing tech occupation, with 50% growth in job postings over the previous year. LinkedIn included ‘data engineer’ as one of its top 15 emerging jobs in the US in 2020. This is unsurprising when you consider that the International Data Corporation (IDC) recently predicted that more than 59 zettabytes (ZB) of data would be created, captured, copied, and consumed in the world during 2020. It also forecasts that the data created over the next three years will be more than that created over the past 30 years.  

Just as demand for engineers rises in line with the expansion of Big Data, so the skills and responsibilities of the job are expanding as data handling becomes more complex. With mountains of real-time data coming in, data engineers need more than the simple ‘warehousing’ and ETL (extract, transform, load) functions that were a big part of the job description ten years ago. 

It also means that companies that think their data scientists will do as good an engineering job as a specialist, may end up regretting it. While there are obviously overlapping skills and responsibilities, the volume and speed of data today means data scientist and data engineer are best viewed as two separate roles on a close-knit team. The data scientists will typically have a math and statistics background, applying these skills to create advanced analytical models, sometimes using ML and AI. The data engineers will usually have a deeper background in programming, software engineering, database management and systems creation. Their core strengths, as Big Data Institute Managing Director Jesse Anderson puts it on O’, will be in creating software solutions around big data.  

Key Skills A Data Engineer Needs

As we’ve already mentioned, data engineers are required to organize huge volumes of raw data coming from disparate sources into “warehouses” of uniform, clean and reliable data ready for modeling/analysis. To do this, they need to construct robust pipelines to transport data quickly and accurately, while also being responsible for the maintenance and updates of the systems they build. 

Though responsibilities will vary from job to job, here are just some of the most important and sought after tech skills for data engineers today: 

Software Engineering - This is at the core of data engineering, and extensive knowledge of software architecture and distributed systems is essential when building the pipelines and storage infrastructure for Big Data.

Programming & Database Management - Python is the most popular language used in data science, largely due to its relative simplicity, flexibility, and widespread community participation. Other common examples include Java and R. Extensive knowledge of database languages and tools. Expertise with databases (SQL and NoSQL in particular) is also essential.

Data processing - This is a fundamental part of a data engineer’s role, and requires knowledge of a number of useful tools. Apache Spark is a powerful open-source framework and analytics engine for processing big data sets from multiple sources. Hadoop is another key tool that allows for the distributed processing of large data sets across clusters of computers - it is often used with Hive, a data warehouse infrastructure tool that is also a part of the Apache suite.

Cloud Platforms/Servers - According to data expert and writer Jeff Hale, knowledge of Amazon Web Services (AWS) was cited in 43% of data engineer job postings during 2019. This is only likely to grow as more and more organizations switch to cloud services, so data engineers should be comfortable handling data on these platforms. 

Analytics - Although data scientists take the lead on analyzing data, a top-level data engineer will also develop some learning in this field as it will help them build better infrastructure and deliver higher quality datasets. 


Of course, on top of mastering these tech skills and tools, the best data engineers also need to develop strong non-tech capabilities. These include an understanding of core business needs, strong communication skills, and the ability to work well in a team. These are the things we look for at Jobsity when hiring well-rounded and talented data engineers and scientists, making sure they are ready to hit the ground running at your organization. To find out more about the possibilities of working with Jobsity to expand your tech capabilities, just drop us a line!


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Jan 28, 2021
Santiago Castro
Santiago Castro

Santiago, COO at Jobsity, has been working on the web development industry for more than 15 years, assuming a variety of roles as UX/UI web designer, senior frontend developer, technical project manager and account manager, he has achieved a deep understanding of the development process and management, and developed strong communication skills with groups and clients. At present, Santiago runs the operations of Jobsity, managing offices in the United States, Ecuador and Colombia, leading a team of more than 100 developers, working on major projects for clients like NBC, GE, Bloomberg, Cargill, Pfizer, Disney and USA Today.

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