The Evolving Role of Data Engineers

Over the years, data and its related fields have undergone a paradigm shift. Earlier, focus revolved around the retrieval of useful insights, but recently,  data management has gained recognition. As a result, the role of data engineers has slowly come into the spotlight. 

Data engineers lay down the foundation of a database and its architecture. They assess a wide range of requirements and apply relevant database techniques to create a robust architecture. Afterward, the data engineer begins the implementation process and develops the database from scratch. After periodic intervals, they also carry out testing to identify any bugs or performance issues. A data engineer is tasked with maintaining the database and ensuring that it works smoothly without causing any disruption. When a database stops working, it brings a halt to the associated IT infrastructure. The expertise of a data engineer is especially needed to manage large-scale processing systems where performance and scalability issues need continuous maintenance. 

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Data engineers can also support the data science team by constructing dataset procedures that can help with data mining, modeling, and production. In this way, their participation is crucial in enhancing the quality of data. 

Data Engineer Roles and Responsibilities

Data engineers are expected to perform the following duties.

Work on Data Architecture

They use a systematic approach to plan, create, and maintain data architectures while also keeping it aligned with business requirements. 

Collect Data

Before initiating any work on the database, they have to obtain data from the right sources. After formulating a set of dataset processes, data engineers store optimized data. 

Conduct Research

Data engineers conduct research in the industry to address any issues that can arise while tackling a business problem. 

Improve Skills

Data engineers don’t rely on theoretical database concepts alone. They must have the knowledge and prowess to work on any development environment regardless of their programming language. Similarly, they must keep themselves up-to-date with machine learning and its algorithms like the random forest, decision tree, k-means, and others. 

They are proficient in analytics tools like Tableau, Knime, and Apache Spark. They use these tools to generate valuable business insights for all types of industries. For instance, data engineers can make a difference in the health industry and identify patterns in patient behavior to improve diagnosis and treatment. Similarly, law enforcement engineers can observe changes in crime rates. 

Create Models and Identify Patterns

Data engineers use a descriptive data model for data aggregation to extract historical insights. They also make predictive models where they apply forecasting techniques to learn about the future with actionable insights. Likewise, they utilize a prescriptive model, allowing users to take advantage of recommendations for different outcomes. A considerable chunk of a data engineer’s time is spent on identifying hidden patterns from stored data. 

Automate Tasks

Data engineers dive into data and pinpoint tasks where manual participation can be eliminated with automation.

How Data Engineers Bring Value to Organizations

Data engineers extract and acquire data from different sources, including the database – it can be SQL Server, Oracle DB, MySQL, Excel, or any other data storing or processing software. Afterward, they apply algorithms on this data and make it useful so it can assist different departments like marketing, sales, finance, and others to introduce more productivity in their work. 

Data engineers are entrusted with supervising the analytics in an organization. Data engineers equip your data with velocity. Businesses find it hard to make real-time decisions and accurately estimate metrics like fraud, churn, and customer retention. For instance, data engineers can help an e-commerce business learn which of their products will have more demand in the future. Similarly, it can allow them to target different buyer personas and deliver more personalized experiences to their customers.

As the world moves towards big data, data engineering can manage and leverage it to produce accurate predictions. By providing well-governed data pipelines, data engineers can improve machine learning and data models. 

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How to Become a Data Engineer

If you want to get hired for the role of a data engineer, enroll yourself for a Bachelor’s degree in Computer Science, Mathematics, or any other IT related course of study. Certifications can provide further icing on the cake. This job requires a lot of understanding regarding theoretical aspects. 

You should have knowledge about database systems and data warehousing. Similarly, you should know how to perform a comparative analysis of data stores. Get your head around relational and non-relational database designs. This means having proficiency in both SQL and NoSQL domains. 

During your studies, experiment with personal projects and solve problems. Start from small projects and utilize different concepts one-by-one. Gradually, take part in open source projects to polish your skills. Learning the following skills will open new doors for you.

SQL

SQL serves as the fundamental skill-set for data engineers. You cannot manage an RDBMS (relational database management system) without mastering SQL. To do this, you will need to go through an extensive list of queries. Learning SQL is not just about memorizing a query. You must learn how to issue optimized queries.

Data Warehousing

Get a grasp of building and working with a data warehouse; it is an essential skill. Data warehousing assists data engineers to aggregate unstructured data, collected from multiple sources. It is then compared and assessed to improve the efficiency of business operations.

Data Architecture

Data engineers must have the required knowledge to build complex database systems for businesses. It is associated with those operations that are used to tackle data in motion, data at rest, datasets, and the relationship between data-dependent processes and applications.

Coding

To link your database and work with all types of applications – web, mobile, desktop, IoT – you must improve your programming skills. For this purpose, learn an enterprise language like Java or C#. The former is useful in open source tech stacks, while the latter can help you with data engineering in a Microsoft-based stack. However, the most necessary ones are Python and R. An advanced level of Python knowledge is beneficial in a variety of data-related operations. 

Operating System

You need to become well-versed in operating systems like UNIX, Linux, Solaris, and Windows. 

Apache Hadoop-Based Analytics

Apache Hadoop is an open-source platform that is used to compute distributed processing and storage against datasets. They assist in a wide range of operations, such as data processing, access, storage, governance, security, and operations. With Hadoop, HBase, and MapReduce, you can further your skill sets. 

Machine Learning

Machine learning is mostly linked to data science. However, if you can have some idea of how data can be used for statistical analysis and data modeling, it will serve you well during your job as a data engineer. 

Are you prepared enough for a career in Big Data? Well try answering this Big Data and Hadoop Developer Practice Test and find out!

Getting Certified For Your Data Engineer Career Path 

In a few years, the demand for data engineer role has risen astronomically. Organizations are actively looking for data engineers to address their data woes. This skill set is high in demand, and it is far from being oversaturated like other fields. Those who pick up these skills have an opportunity to make high salaries. For this purpose, the right certification can turn out to be quite useful. 

If you want to improve your data engineering skill set and stand out from the competition, consider getting professional certification from Simplilearn. 

About the Author

Ronald Van LoonRonald Van Loon

Named by Onalytica as the world's #1 influencer in Data and Analytics, Automation, and the Future Economy (Tech), Ronald is one of the top thought leaders in Data Science and Digital Transformation. He’s a popular keynote speaker and an author for numerous leading Big Data & Data Science websites.

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