A report suggests data engineer is the fastest-growing job in technology, with over 50% year-over-year growth in the number of open positions.
In 2012, Harvard Business Review chose ‘data scientist’ as the sexiest job of the 21st century. Data scientists have been one of the most sought after jobs in the last few years. But now, data engineering jobs are poised to give data scientists tough competition.
While some of their activities might overlap, data engineers are primarily about moving and transforming data into pipelines for the data science team. Put it simply, data engineers have three critical tasks to perform — design, build and arrange data pipelines. In contrast, data scientists analyse, test, aggregate and optimise data.
While data scientists have been getting much attention lately, data engineers lay the foundation in most projects. For instance, maintaining a data pipeline is often the most crucial step in generating insights from data.
Data Engineering At The Core
Data engineers essentially collect, generate, store, enrich and process data in real-time or in batches. Data engineering involves building data infrastructure and data architecture.
Data engineers require experience in software engineering, programming languages, and a firm grip on core technical skills. Understanding ETL, SQL, and programming languages such as Java, Scala, C++, and Python are desired.
Some of the tasks data engineers might be required to do are:
- Designing big data infrastructure
- Preparing data to be analysed
- Building and optimising data pipelines from the ground up
- Assembling large and complex data sets to meet functional and non-functional demands
- Build the infrastructure necessary for optimal extraction
- Automating manual process
- Optimising data delivery
- Redesigning infrastructure for greater scalability
- Assisting data scientists in building and optimising products
Meanwhile, data scientists are responsible for finding solutions with the available data and communicating it with the team. Data engineers carry the core technical work, and data scientists work on the processed data. What most data scientists do today is essentially the work of data engineers.
In High Demand
A report suggests data engineer is the fastest-growing job in technology, with over 50% year-over-year growth in the number of open positions. In 2019, it had witnessed an 88.3 percent increase in postings over the past twelve months. Another report suggests the demand for data engineers have been on the rise since 2016.
The data science strategy in an organisation deals with data infrastructure, data warehousing, data mining, data modelling, data crunching, and metadata management, most of which are carried out by data engineers. Studies suggest most data science projects fall through as data engineers and data scientists find themselves at cross purposes. Many companies fail to recognise the importance of hiring data engineers.
While most companies are starting to realise the importance of data engineers, the talent shortage is all too real. The demand-supply gap and the soaring value of data engineers have opened up plum posts for the data engineers. Reports suggest the number of job openings for data engineers is almost five times higher than the job openings for data scientists. Data engineers’ demand has begun to outpace the demand for data scientists by 2:1.
And, in most cases, their average salaries are surprisingly high compared to data scientists. Many companies are paying 20-30% more to data engineers than to data scientists. Data engineers are soon becoming the highest-paid talent, and their salaries continue to grow at a rapid pace.
Apart from the focus of companies to designate data preparation tasks to data engineers, the fact that most businesses are migrating to the cloud has further increased the demand for data engineers.
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