Data and every professional field that depends on it have seen a significant paradigm shift in the last decade. Earlier, the focus was always on deriving actionable insights from raw data. However, today data management has become a significant point of attention. Consequently, recognition of and the demand for data engineers has gradually increased. 

A data engineer prepares the foundation for databases and their architecture. They evaluate various needs and use the appropriate database methodologies to install robust architecture. Following that, they start implementing the infrastructure and develop entire databases from the ground up. At regular intervals, data engineers also perform testing to identify bugs or performance issues and the system functions smoothly without disruption. If a database stops functioning, it brings an entire IT infrastructure to a standstill. A data engineer’s technical expertise is vital for enterprises to manage large-scale data processing systems where scalability and performance issues require continuous maintenance. 

A data engineer job description also includes supporting data science teams by creating dataset procedures designed to help with data modelling, mining and production. 

Data Engineer Role Types:

Dataquest has mapped out three primary areas or roles that a data engineer’s responsibilities fall under: 

1. Generalist: 

A generalist is generally a data engineer in a small company or on a small team. This type of data engineer performs various roles since they are one of the rare data-focused specialists within the organisation. Generalists are usually responsible for all the steps along the data processing journey, from data management to data analysis. According to Dataquest, this role is perfect for professionals who want to switch from data science to data engineering since small businesses are not focused on scaling up using data engineering. 

2. Pipeline-centric: 

Generally, these data engineers work with medium companies. They work closely with data scientists to make all enterprise data usable. A pipeline-centric data engineer must have a comprehensive understanding of computer science and distributed systems. 

3. Database-centric: 

Managing data flow requires a specialist’s attention and expertise in large or global companies. It is in these companies where a data engineer works on analytics databases. A database-centric engineer works on data warehouses across several databases and is responsible for table schema development. 

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Data Engineer Responsibilities:

Data engineers manage and organise enterprise data while looking for inconsistencies or trends that could affect business objectives. This role is highly technical and requires skills and experience in domains such as mathematics, computer science and programming. However, a data engineer also needs soft skills when sharing data trends with other non-technical stakeholders, colleagues and team members. 

Some of the most common tasks that a data engineer performs include:

• Developing, testing, maintaining and constructing architectures
• Aligning architecture with existing business needs
• Acquiring data
• Developing processes for data sets
• Using programming languages and tools
• Exploring new ways to enhance efficiency, quality and data reliability
• Conducting research for business and industry queries
• Using large data sets to resolve business problems
• Deploying sophisticated data analytics systems, statistical methods and machine learning
• Preparing data for prescriptive and predictive modelling
• Identifying patterns using data
• Using data to identify operations that can easily be automated
• Delivering updates based on advanced analytics to stakeholders

Reasons to Choose a Data Engineering Career:

As long as businesses have data to analyse, use and process, data engineers will remain high in demand. According to one Dice Insights report from 2019, data engineering is trending as one of the data industry’s top job roles. It beats out web designers, computer scientists and even data architects. According to LinkedIn, it was the fastest rising job role in 2021. 

Salary of a Data Engineer:

Besides being a highly-demanded and trending role, data engineers also are remunerated handsomely. In the US, the average annual salary of a data engineer is $111,933, according to Glassdoor. Some even earn as much as $164,000 per annum. Compared to other data-focused jobs, where Database administrators earn $81,444 per annum and data analysts earn $68,000, data engineer skills pay off significantly.

Data Engineer Career Path:

Data engineering isn’t always an entry-level role. Instead, many data engineers start off as software engineers or business intelligence analysts. As you advance in your career, you may move into managerial positions or become a data architect, solutions architect, or machine learning engineer.

Steps to Becoming a Data Engineer:

Besides just getting an undergraduate degree, aspiring data engineering professionals can take several steps to reach a successful career in the field. Some of the steps you can follow are:

Developing Data Engineering Skills:

The first step is developing your skills and sharpening your foundational understanding of domains like coding, cloud computing, and database designing. The domains to master are:

1. Coding: This role requires fluency in programming languages like NoSQL, SQL, Java, R, Python and Scala.
2. Relational/non-relational databases: A database ranks high among all existing data storage solutions. Both relational and non-relational databases are essential, so make sure you understand them. 
3. Extract, transform, load (ETL) systems: ETL refers to the process used to transfer data to a unified repository, such as a data warehouse from databases or other data storage options. Some standard tools used for ETL include Stitch, Xplenty and Talend . 
4. Data storage services: Different types of data have different storage needs, primarily when you work with Big Data. While designing data solutions for an organisation, you should know, for instance, when a data lake should be used and when you need a data warehouse.
5. Scripting and automation: Automation is a fundamental component when working with Big Data as the volumes of data that organisations collect are high. Data engineers should know how to write scripts for repetitive task automation.
6. Machine Learning: Data scientists are usually the ones primarily focused on Machine Learning. However, for roles like generalists, having a basic understanding of Machine Learning concepts helps you understand what your team’s data scientists need.
7. Big Data tools: A data engineer doesn’t work only with normal data but also dabbles with Big Data from time to time. Big Data technologies and tools continue advancing and vary from one company to another, with the most widely used ones being MongoDB, Kafka and Hadoop.
8. Cloud computing: With more and more businesses adopting cloud services over physical servers, understanding cloud computing and storage is a huge plus. Freshers with minimum or no experience can take up an online course on AWS or Azure to better understand. 
9. Data security: Some businesses use dedicated teams for data security. But most organisations still rely on data engineers for secure data storage and management.

Also Read: Top Data Engineer Interview Questions and Answers

Get a Certification:

Earning a professional certification validates a professional’s skills before hiring managers and employers. You also hone your understanding and skills as you prepare for the certification exam. One smart way to find out which certifications you should apply for is by checking out certain job listings for data engineering roles you would consider in the future. If a certain qualification is repeated in several places, you should probably opt for that one. 

Create a Portfolio of Data Engineering Projects:

Portfolios are generally integral to professionals’ credibility before recruiters. A portfolio of work shows your hiring manager and potential employer everything you are capable of. You have an advantage when you add your past data engineering work to a personal website. You can easily make one with Squarespace or Wix. You can also choose to share your portfolio on a website like Behance, GitHub or LinkedIn.

Go for an Entry-level Role First:

Most data engineers don’t start their careers in this position. Get your career off the ground with an entry-level job profile, such as a database administrator or a BI analyst. 

If you want to get a data engineering certification, there’s no better time to start. Enrol in a training course on Koenig today and take your career to the next level.