With the exponential growth of data in today’s digital world, data engineering has become an essential component of data management. A data engineer is a critical member of the data team who plays a crucial role in building and maintaining a company’s data infrastructure. In this blog, we will explore the roles and responsibilities of a data engineer, the skills required to excel in this field, and how data engineers help organizations make informed decisions based on analyzing large datasets.

Data engineers work to build various systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.

These are some common tasks you might perform when working with data:

• Acquire datasets that align with business needs
• Develop algorithms to transform data into useful, actionable information
• Build, test, and maintain database pipeline architectures
• Collaborate with management to understand company objectives
• Create new data validation methods and data analysis tools
• Ensure compliance with data governance and security policies

Data engineers in smaller companies often take on more data-related tasks in a generalist role. However, some bigger companies have data engineers dedicated to building data pipelines, while others are focused on managing data warehouses, populating warehouses with data, and creating table schemas to keep track of where data is stored.

Although “data analyst” and “data engineer” are often used interchangeably, they refer to two distinct roles in the data industry. The main difference between a data analyst and a data engineer lies in the nature of their work, their skills, and their goals.

A data analyst analyzes and interprets data, typically using statistical and analytical tools to extract insights and make recommendations. Data analysts are often tasked with presenting data in an easily digestible format to stakeholders, such as executives or business leaders, to aid decision-making.

On the other hand, a data engineer focuses on designing, building, and maintaining the data infrastructure that enables data analysis. Data engineers ensure the data is stored, processed, and delivered accurately and efficiently. They use tools and technologies such as SQL, ETL (Extract, Transform, Load) processes, and data warehousing to ensure data quality, security, and availability.

While both roles require a strong foundation in data and analytical skills, a data analyst typically has more statistical modeling, data visualization, and data storytelling expertise. In contrast, a data engineer has more experience in database architecture, software engineering, and data pipeline development.

A career in this field can be both rewarding and challenging. You’ll play an important role in an organization’s success, providing easier access to data that data scientists, analysts, and decision-makers need to do their jobs. You’ll rely on your programming and problem-solving skills to create scalable solutions.

Data engineers will be in demand as long as there is data to process. Dice Insights reported in 2019 that data engineering is a top trending job in the technology industry, beating out computer scientists, web designers, and database architects. However, LinkedIn listed it as one of its jobs on the rise in 2021.

The career path for a data engineer typically involves a combination of education, practical experience, and ongoing learning and professional development. Here are some common steps:

1. Education: A bachelor’s degree in computer science, information technology, or a related field is often required for a data engineering role. In addition, coursework in database management, data structures, algorithms, and programming languages like Java, Python, and SQL may be helpful.
2. Entry-level position: After their education, data engineers may start in an entry-level position, such as a data analyst or database developer. In these roles, they will gain hands-on experience working with data and databases and develop their skills in programming languages and tools used in data engineering.
3. Mid-level position: As they gain experience, data engineers may progress to mid-level positions such as data engineer or ETL (Extract, Transform, Load) developer. In these roles, they are responsible for designing and building data pipelines, managing data storage systems, and ensuring the accuracy and integrity of data.
4. Senior-level position: With further experience and expertise, data engineers may advance to senior-level positions such as lead data engineer or data architect. In these roles, they oversee the design and implementation of complex data systems, collaborate with cross-functional teams, and develop strategies to optimize data infrastructure and processes.
5. Management or specialized roles: Some data engineers may move into management roles, such as director of data engineering or VP of data. Others may specialize in specific areas like machine learning, data science, or cloud computing.

Throughout their career, data engineers should continue to learn new technologies, tools, and techniques to stay up-to-date with the rapidly evolving field of data engineering. They may also pursue professional certifications or attend conferences and training sessions to deepen their knowledge and skills.

With appropriate skills and knowledge, you can launch or advance a rewarding career in data engineering. Many data engineers have a bachelor’s degree in computer science or a related field.

By earning a degree, you can build a foundation of knowledge you’ll need in this quickly-evolving field. Consider a master’s degree for the opportunity to advance your career and unlock potentially higher-paying positions. Besides earning a degree, you can take several other steps to set yourself up for success.

1. Develop your data engineering skills.

Learn the fundamentals of cloud computing, coding skills, and database design as a starting point for a career in data science.

• • Coding: Proficiency in coding languages is essential to this role, so consider taking courses to learn and practice your skills. Standard programming languages include SQL, NoSQL, Python, Java, R, and Scala.
  • Relational and non-relational databases: Databases rank among the most common solutions for data storage. You should be familiar with relational and non-relational databases and how they work.
  • ETL (extract, transform, and load) systems: ETL is the process by which you’ll move data from databases and other sources into a single repository, like a data warehouse. Standard ETL tools include Xplenty, Stitch, Alooma, and Talend.
  • Data storage: Not all types of data should be stored the same way, especially in big data. As you design data solutions for a company, you’ll want to know when to use a data lake versus a data warehouse.
  • Automation and scripting: Automation is necessary to work with big data simply because organizations can collect so much information. You should be able to write scripts to automate repetitive tasks.
  • Machine learning: While machine learning is more the concern of data scientists, it can be helpful to grasp the basic concepts better to understand the needs of data scientists on your team.
  • Big data tools: Data engineers don’t just work with regular data. They’re often tasked with managing big data. Tools and technologies are evolving and vary by company, but some popular ones include Hadoop, MongoDB, and Kafka.
  • Cloud computing: You’ll need to understand cloud storage and cloud computing as companies increasingly trade physical servers for cloud services. Beginners may consider a course in Amazon Web Services (AWS) or Google Cloud.
  • Data security: While some companies might have dedicated data security teams, many data engineers are still tasked with securely managing and storing data to protect it from loss or theft.

2. Get Certified

A certification can validate your skills to potential employers, and preparing for a certification exam is an excellent way to develop your skills and knowledge. Options include the Associate Big Data Engineer, Cloudera Certified Professional Data Engineer, IBM Certified Data Engineer, or Google Cloud Certified Professional Data Engineer.

Check out some job listings for roles you may want to apply for. If you notice a particular certification is frequently listed as required or recommended, that might be an excellent place to start.

3. Build a portfolio of data engineering projects.

A portfolio is often a key component in a job search, showing recruiters, hiring managers, and potential employers what you can do. You can add data engineering projects you’ve completed independently or as part of coursework to a portfolio website (using a service like Wix or Squarespace).

Alternatively, post your work to the Projects section of your LinkedIn profile or a site like GitHub—both free alternatives to a standalone portfolio site.

This blog discusses how a data engineer is a crucial data team member. They are responsible for designing, building, and maintaining the data infrastructure to support data-driven decision-making. Their responsibilities span from developing data pipelines to managing databases and ensuring data quality. The role of a data engineer is becoming increasingly important as businesses continue to rely on data to inform their strategic decisions. The skills required for a data engineer include expertise in database design, programming languages, cloud computing, and data management tools.

Royal Cyber data experts have been building such innovative solutions for years. Our certified consultants and trained data engineers can help you get the most out of your data. Feel free to contact our team if there are any queries.