How to Become a Data Engineer

Have you ever wondered how companies like Google, Facebook, and Amazon manage and analyze massive amounts of data every day? The answer lies in the expertise of data engineers. These professionals are responsible for building and maintaining the infrastructure that allows data scientists, analysts, and other stakeholders to extract valuable insights from complex datasets. For those with a passion for problem-solving and a strong interest in technology, a career in data engineering might be perfect. Read on to understand the steps to becoming a data engineer, including education, skills, hands-on experience, and more.

What you’re about to learn in this guide:

  • Becoming a data engineer requires technical proficiency, formal education, and hands-on experience.

  • Core skills include programming languages, big data tools, and cloud computing platforms.

  • Job opportunities are plentiful with potential for career progression into management or leadership positions.

Steps to Becoming a Data Engineer

A foundation in computer science and data analytics is necessary for a data engineer.

A strong foundation in computer science and data analytics is a fundamental start to becoming a data engineer. Most data engineers hold a minimum of a bachelor’s degree in a relevant field, such as computer science or data science. Combining technical, problem-solving, and collaboration skills, successful data engineers stay updated on the latest technologies and industry trends.

While a bachelor’s degree is typically the minimum requirement, many aspiring data engineers also pursue master’s degrees to deepen their knowledge and specialize in a particular area. Master’s programs often include more advanced coursework and offer opportunities for internships or research projects to enhance a resume. Alongside formal education, mastering programming languages, big data tools, and cloud computing platforms is pivotal.

Data Engineer Education and Degrees

Acquiring a master's or advanced degree can help data engineers excel along their career journey.

A career in data engineering typically begins with a bachelor’s or master’s degree in computer science, data science, or a related field. These degrees provide a strong foundation in the principles of data engineering, including data storage, data pipelines, and data analysis.

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The growth of data engineering jobs will continue to increase, with it being among LinkedIn’s emerging jobs as of 2020 with an anticipated 33 percent annual growth. The average data engineer salary is around $103,000 annually, making it a lucrative career choice.

Advanced degree programs and additional courses can provide more professional and advancement opportunities for those aspiring to build a career in the field of data engineering.

Bachelor’s Degree Programs

Bachelor’s degree programs in data science, computer science, or information systems provide a strong foundation for aspiring data engineers. These programs typically include courses in data structures, algorithms, database management, and coding. Bachelor’s degree programs in data science or data analytics will provide necessary education for the field. These include real-time analytics, mining software, machine learning applications, business intelligence, database design, and programming languages. Additional courses may delve into more specific areas like networking modeling, data patterns, data structure, file management, data manipulation, and data security practices.

Even though a bachelor’s degree suffices for entry into data engineering, many professionals opt for a master’s degree to further specialize and advance their careers. Master’s degree programs will cover more advanced topics and provide additional opportunities for practical experience.

Master’s Degree Programs

Obtaining an advanced degree will help boost the job prospects for a data engineer.

Master’s degree programs in data science or data engineering offer advanced coursework and specialization options for those looking to build their careers. These programs typically cover topics such as:

  • Predictive analysis

  • Data trends

  • Decision support

  • Statistical analysis

  • Machine learning theory

  • Data architecture

  • Forecasting

Some companies may prefer candidates with a master’s degree for non-managerial positions, making it a valuable investment in a future career.

Besides advanced coursework, master’s degree programs frequently offer internships and research project opportunities. These experiences can offer students practical, hands-on experience in the field and help them make valuable industry connections. This gives students content for their work portfolio when it’s time to interview for a data engineering job.

Essential Data Engineering Skills

Data engineering requires an understanding of programming languages, big data tools, and data platforms.

Excelling in data engineering requires a robust skill set including proficiency in programming languages, familiarity with big data tools, and adeptness in using cloud computing platforms. These skills enable data engineers to build and maintain data systems, manage databases, and work effectively with large datasets.

In addition, having a strong understanding of data warehousing concepts and ETL (extract, transform, load) processes is crucial. With a background in programming languages, big data tools, and cloud computing platforms, data engineers are well-prepared for a successful career.

Programming Languages

Data engineers rely on programming languages like Java, Python, and SQL to construct and manage data systems. These languages allow data engineers to interface with database management systems and create complex data pipelines. Proficiency in SQL is particularly important for data engineers, as it helps them issue optimized queries and work effectively with databases.

In addition to these core languages, data engineers should also be familiar with a myriad of other programming languages and tools that can enhance their capabilities. Frameworks like Apache Airflow and Apache Spark are essential for complex data projects.

Big Data Tools

Managing and processing large datasets necessitates the use of big data tools such as Hadoop, Spark, and Kafka. These tools enable data engineers to work with massive amounts of data and derive valuable insights for their organizations.

Familiarity with stream processing frameworks such as Flink, Kafka Streams, and Spark Streaming is also beneficial for data engineers. Keeping up-to-date with the latest data engineering tools is essential for staying competitive in the job market. Some of the most recent data engineering tools include Snowflake, dbt, and Airflow.

Cloud Computing Platforms

Data engineers utilize cloud computing platforms such as AWS, Azure, and Google Cloud Platform to construct scalable and efficient data infrastructure. These platforms provide a wide range of services, including storage, databases, networking, software, analytics, and more, making them an indispensable part of modern data engineering.

Data engineers are responsible for constructing and preserving data pipelines on these platforms to facilitate the collection, storage, and analysis of data while ensuring that data is secure and compliant with regulations.

To access various data resources and build effective ETL data pipelines, data engineers must be proficient in SQL and big data technologies. This combination of skills allows data engineers to work seamlessly with cloud computing platforms and deliver high-quality data solutions.

Gaining Hands-On Experience

Internships and personal projects can help data engineers build a robust portfolio.

Building a robust data engineering portfolio necessitates hands-on experience, which can be gained through internships and personal projects. These experiences allow data engineers to apply theoretical knowledge to real-world problems and demonstrate a technical proficiency to potential employers. Additionally, gaining practical experience can help in develop the problem-solving skills that are essential for a successful data engineering career.


Internships provide valuable real-world experience and networking opportunities for aspiring data engineers. By working on real projects and collaborating with experienced professionals, they can gain a deeper understanding of data engineering concepts and develop the practical skills required for success in the field.

Job platforms often offer data engineering internships, or they can be found through corporations or universities.

Internships also offer the opportunity to:

  • Make valuable industry connections that can lead to job opportunities and career advancement

  • Work on cutting-edge projects and learn from industry experts at well-known companies like Amazon, Tesla, and Point72.

  • Determine which areas within data engineering might be appealing.

Personal Projects

Personal projects are another opportunity to showcase technical skills and problem-solving abilities to potential employers. By working on real-world problems and utilizing industry-standard tools, job seekers can demonstrate expertise and commitment to the field. Examples of personal projects for data engineers include:

  • Constructing a real-time streaming data processing pipeline

  • Building a data warehouse

  • Analyzing sentiment on stocks

  • Scraping data from websites using Python and other tools

In addition to showcasing skills, personal projects can also help with gaining practical experience in a wide range of data engineering tasks. This experience is invaluable for building a strong data engineering portfolio and being well-prepared for the challenges of a professional data engineer role.

Certifications for Data Engineers

Many international organizations offer certifications in data engineering skills.

Earning certifications from organizations such as Google, IBM, and Microsoft can affirm data engineering skills and bolster job prospects. These certifications evaluate understanding and abilities along industry standards, making them a valuable addition to a resume. Obtaining a data engineer certification demonstrates an ability to effectively manage and utilize large data sets to generate accurate forecasts.

In addition to enhancing job prospects, certifications can also improve data engineer salary prospects. Many employers value certified professionals, as they have demonstrated their commitment to ongoing professional development and staying current with industry standards.

Finally, earning a Data Engineer certification with Python can improve salary prospects and validate your skills.

Job Opportunities and Career Paths

It may take time to find the right type of data engineering job.

Data engineers can explore a range of jobs, including roles like data architect, database administrator, and big data engineer. All positions offer the possibility of career progression into management or leadership positions. Data engineers are responsible for sourcing, cleaning, and transforming raw data to make it usable and comprehensible for other data teams within an organization. The demand for data engineer roles has seen a significant increase in recent years, making it an attractive career choice for those with the right skills and qualifications.

Data engineers can progress to positions such as senior data engineer, data architect, or data engineering manager. With experience and a strong skill set, they can advance their career and take on more complex and challenging projects, ultimately contributing to the success of an organization by enabling better data-driven decision-making.

Networking and Industry Connections

Networking is one way to stay current with trends and make contacts.

Maintaining a robust network and industry connections assists data engineers in keeping abreast of the latest trends, technologies, and job opportunities. Attending conferences, meetups, hackathons, and industry-specific events allow data engineers to broaden their professional network and gain insights into the latest developments in data engineering.

Networking also offers the opportunity to connect with other professionals in the field, which can lead to job opportunities and career progression. To optimize networking efforts, consider attending events and sessions, engaging in conversations, and following up with contacts. Additionally, be prepared and have a specific objective when networking to make the most of these opportunities.

Preparing for a Data Engineer Interview

Sharing relevant experience and a robust portolio will help job seekers during the interview.

Preparation for data engineering interviews encompasses refining technical skills, honing problem-solving abilities, and articulating past experiences and projects. Before the interview, become refamiliar with programming languages, big data tools, and cloud computing platforms. These skills are essential for success in becoming a data engineer.

During the interview, be prepared to discuss:

  • Relevant experiences and projects related to being a data engineer.

  • How theoretical knowledge has been applied to real-world problems.

  • Any technical proficiency in data engineering.

Demonstrating these skills and experiences will help job seekers stand out from other candidates and increase the chances of landing a data engineering position.

Data Engineer vs. Data Scientist

Both data engineers and data scientists are sought positions and growing fields.

Despite both data engineers and data scientists working with data, their roles and responsibilities exhibit considerable differences.

Data engineers focus on:

  • Building and maintaining data infrastructure

  • Ensuring that data is properly collected, stored, and processed

  • Working with tools and technologies such as Hadoop, Spark, and SQL to construct and sustain big data architectures

Data scientists, on the other hand, analyze data and interpret it to derive insights and make data-driven decisions. The success of data scientists relies heavily on the work of data engineers. Without the tools and infrastructure created by data engineers, data scientists would not be able to effectively analyze and interpret the data they need to make informed decisions for their organizations.

As a result, both data engineers and data scientists play essential roles in the modern data-driven business landscape.


In conclusion, becoming a successful data engineer requires a combination of education, skills, hands-on experience, and networking. It’s possible to build a strong foundation and a rewarding career in data engineering by pursuing relevant degrees, developing essential skills in programming languages, big data tools, and cloud computing platforms, and gaining practical experience through internships and personal projects.

As the demand for data engineers continues to grow, there has never been a better time to explore this exciting career path.

Frequently Asked Questions

What qualifications are needed to be a data engineer?

To be a data engineer, applicants need to possess a bachelor’s degree in computer science or a related field and have proficiency in programming languages such as Python, Java, and SQL. Experience with big data technologies like Hadoop, Spark, and Kafka as well as cloud computing platforms like AWS, Azure, and Google Cloud Platform is beneficial. Additionally, understanding the concepts behind building a robust pipeline is necessary.

How long does it take to become a data engineer?

Becoming a data engineer usually takes four to five years, depending on the chosen pathway. It typically takes about four years to earn a bachelor’s degree and up to six months to obtain job training for an entry-level position. Additional certifications can help data engineers advance faster in this field.

Is it hard to become a data engineer?

Becoming a data engineer can be hard, but with the right skills and job opportunities, you’ll find plenty of freedom to craft your dream role.

Can a computer science major become a data engineer?

Yes, a computer science major can become a data engineer. With a degree in computer science or related field, it’s an easy transition to build upon the foundational knowledge needed for this rapidly evolving field and transition into becoming a data engineer. A master’s degree may further open up higher-paying positions.

What is a master data engineer?

A master data engineer is a professional who specializes in creating, managing, and maintaining large datasets for use in data-driven systems. They integrate messy data into clean, usable datasets. Then they efficiently organize and retrieve large data, and creatively solve data-related analytical problems to support computation and data analytics functions.

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