Over the years, data and related fields have undergone a paradigm shift. Earlier the focus revolved around extracting actionable insights, but recently data management has gained recognition. As a result, the role of data engineers is slowly coming into the limelight.
Data engineers lay the foundations of a database and its architecture. They evaluate a wide range of requirements and apply appropriate database techniques to create a robust architecture. The data engineer then begins the implementation process and develops the database from scratch. After periodic intervals, they also perform tests to identify bugs or performance issues. A data engineer is tasked with maintaining the database and ensuring that it runs smoothly without causing disruptions. When a database goes down, it brings the associated IT infrastructure to a standstill. The expertise of a data engineer is particularly needed to manage large-scale processing systems where performance and scalability issues require continuous maintenance.
Data engineers can also support the data science team by constructing dataset procedures that can assist in data mining, modeling, and production. Thus, their involvement is critical to improving data quality.
Data Engineer Roles and Responsibilities
Here is the list of roles and responsibilities that data engineers are expected to perform:
1. Work on data architecture
They use a systematic approach to planning, creating and maintaining data architectures while keeping them aligned with business requirements.
2. Data Collection
Before starting any work on the database, they need to get data from the right sources. After formulating a set of processes for a set of data, data engineers store optimized data.
3. Do research
Data engineers conduct industry research to address any issues that may arise in addressing a business problem.
4. Improve skills
Data engineers don’t just rely on theoretical database concepts. They must have the knowledge and skills to work in any development environment, regardless of their programming language. Similarly, they need to be up to date with machine learning and its algorithms like random forest, decision tree, k-means and others.
They are proficient in analytics tools such as Tableau, Knime and Apache Spark. They use these tools to generate valuable business insights for all types of industries. For example, data engineers can change the healthcare industry and identify patterns in patient behavior to improve diagnosis and treatment. Likewise, law enforcement engineers can monitor changes in crime rates.
5. Create patterns and identify patterns
Data engineers use a descriptive data model to aggregate data to derive historical insights. They also create predictive models where they apply forecasting techniques to learn about the future with actionable insights. Similarly, they use a prescriptive model, allowing users to benefit from recommendations for different outcomes. A significant portion of a data engineer’s time is spent identifying hidden patterns from stored data.
6. Automate tasks
Data engineers dive into data and identify tasks where manual involvement can be eliminated with automation.
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How do data engineers bring value to organizations?
Data engineers extract and acquire data from a variety of sources, including the database – this could be SQL Server, Oracle DB, MySQL, Excel, or other data storage or processing software. They then apply algorithms on this data and make it useful so that they can help various departments like marketing, sales, finance and others bring more productivity to their work.
Data engineers are entrusted with overseeing analytics in an organization. Data engineers equip your data with speed. It is difficult for businesses to make real-time decisions and accurately measure metrics such as fraud, churn and customer retention. For example, data engineers can help an e-commerce business learn which of their products will have higher demand in the future. Likewise, it can allow them to target different buyer personas and provide more personalized experiences to their customers.
As the world moves toward big data, data engineer certification can manage and use it to create accurate predictions. By providing well-managed data pipelines, data engineers can improve machine learning and data models.
How to become a data engineer?
If you want to be hired for a data engineer role, enroll in a bachelor’s degree in computer science, mathematics or another IT-related course. Certificates can provide additional icing on the cake. This work requires a lot of understanding in terms of theoretical aspects.
Must have knowledge of database systems and data storage. Likewise, you need to know how to benchmark data warehouses. Explore the design of relational and non-relational databases. This means having skills in both SQL and NoSQL domains.
During your studies, experiment with personal projects and solve problems. Start from small projects and use different concepts one by one. Gradually get involved in open source projects to hone your skills. Learning the following skills will open new doors for you.
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Skills needed to become a data engineer
Here is the list of 7 essential skills for a data engineer:
SQL serves as a core skill set for data engineers. You can’t run an RDBMS (relational database management system) without learning SQL. To do this, you will have to go through an extensive list of requests. Learning SQL isn’t just about memorizing a query. You need to learn how to send optimized queries.
2. Data Storage
Get insight into building and working with a data warehouse; this is a basic skill. Data warehousing helps data engineers aggregate unstructured data collected from multiple sources. It is then compared and evaluated to improve the efficiency of business operations.
3. Data architecture
Data engineers must have the necessary knowledge to build complex database systems for businesses. It is related to those operations that are used to deal with data in motion, data at rest, datasets, and the relationship between data-dependent processes and applications.
To connect your database and work with all kinds of applications – web, mobile, desktop, IoT – you need to improve your programming skills. For this purpose, learn an enterprise language such as Java or C#. The former is useful in open source technology stacks, while the latter can help you with data engineering in a Microsoft-based stack. However, the essentials are Python and R. An advanced level of familiarity with Python is beneficial for various data-related operations.
5. Operating system
Must be familiar with operating systems such as UNIX, Linux, Solaris and Windows.
6. Apache Hadoop-based analysis
Apache Hadoop is an open source platform that is used for computing distributed processing and storage against datasets. They support a wide range of operations such as data processing, access, storage, management, security and operations. With Hadoop, HBase and MapReduce you can develop your skill sets.
7. Machine learning
Machine learning is mostly related 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 in your work as a data engineer.
Check out the video below that will help you understand who a big data engineer is, their roles and responsibilities, and the essential skills you need to have to become a big data engineer.
Get certified for your data engineering career
The demand for data engineer roles has grown astronomically over the past few years. Organizations are actively looking for data engineers to address their data issues. This skill set is in high demand and nowhere near as oversaturated as other fields. Those who master these skills have the opportunity to earn high salaries. For this purpose, the right certification can prove to be quite useful.
If you want to improve your data engineering skill set and stand out from the competition, consider getting a professional certification in data engineering from Simplilearn.