5 Data Engineer Certifications: Which One Is Right For You?

Data is one of the world’s most valuable resources. According to Fortune Business Insights, the global market for big data analytics is projected to reach $924.39 billion by 2032, a steep increase from its already low 2023 valuation of $307.51 billion [1].

Data engineers are responsible for designing and building systems for collecting, storing, and analyzing big data applicable to nearly every industry. Many people hoping to break into or enhance their data engineering careers wonder whether they need a certification—and if so, which one suits their career objectives. 

In this article, we’ll guide you through whether you need a data engineer certification or not and answer the question, “Which data engineer certification is right for me?” Afterward, if you're interested in pursuing a data engineering career, consider enrolling in the IBM Data Engineering Professional Certificate.

Do you need a data engineer certification?

Data engineer certifications can be useful credentials at any stage of your career. If you're an early-career professional, you can learn data engineering skills and tools like Google Cloud or Azure in a standardized, comprehensive, and chronological manner. If you're an already established data engineer, you can fill in any gaps in your skills and knowledge base or stay up-to-date on the latest tools and technologies. 

Here are some benefits of adding a certification to your resume or displaying the credential on your LinkedIn profile:

• Validate your data engineering skills

• Demonstrate your dedication to growth in your field

• Verify that your knowledge is current and relevant

• Highlight your qualifications and expertise

Certification vs. certificate

Certifications and certificates sound similar, but they are not quite the same. A certificate is proof that an individual has completed a professional training course. A certification provides proof that an individual took and passed a specific exam. Many certificates allow you to take an exam upon completion that can result in a certification.

Is certification worth it?

Certifications and professional certificates can help aspiring data engineers enter the field or boost their job prospects.

For example, the Google Cloud—Professional Cloud Architect certification was ranked #2 on Skillsoft’s list of the 20 top-paying IT certifications for 2024, with an average salary of $190,204 [2]. This certification is linked to a certificate program, Google Cloud's Preparing for Google Cloud Certification: Cloud Architect Professional Certificate, offered on Coursera, that prepares course takers for the certification exam. Furthermore, research conducted by the US Bureau of Labor Statistics in 2024 found that professionals with either a certification or license earned more on average than those without either and experienced lower levels of unemployment [3].

Still, a certification can be an investment of time and money, so it’s important to make a choice that suits your current career goals.

5 popular data engineer certifications 

There are plenty of certification and certificate programs available for aspiring and established data engineers. Here are some data engineering certificate programs offered by industry leaders like Deeplearning.AI, IBM, Google, and Meta.

1. IBM Data Engineering Professional Certificate 

IBM’s Data Engineering Professional Certificate is a flexible online certificate program that provides learners with job-ready skills, tools, and a portfolio in 15 months or less (based on 4 hours per week). The course is designed to prime you for an entry-level data engineer position. You’ll learn how to use Python to extract, transform, and load (ETL) data, work with relational databases, query data using SQL, and work with big data engines Hadoop and Spark.

Requirements: This program does not require prior data engineering or programming experience or a specific educational background.

2. Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate

Google Cloud’s Cloud Data Engineer Professional Certificate is a flexible online certificate program that provides you with a strong foundation in key big data and machine learning products in Google Cloud in five months or less. It incorporates hands-on labs using Qwiklabs. Designed for professionals with a base-level understanding of data engineering concepts, this certificate program is meant to prepare you for the Google Cloud Certification. Keep in mind that not all companies use Google Cloud, so this is not a certification exam that qualifies you as a data engineer but rather for data engineering on Google Cloud only.  

This is a popular option for a professional certificate in data engineering. According to one survey, 81 percent of certificate graduates felt they could prove cloud skill competency to recruiters, and 85 percent felt more confident in cloud skills [4].

Requirements: This professional certificate is intended for intermediate-level data engineers, so you should have basic proficiency with SQL and experience developing applications using common programming languages.

Cost: The registration fee for the Google Cloud Professional Data Engineer Certification exam is $200. 

3. IBM Data Warehouse Engineer Professional Certificate

IBM’s Data Warehouse Engineering Professional Certificate is a flexible online certificate program that provides learners with job-ready skills, tools, and a portfolio in eight months or less. Designed for data warehouse engineer beginners, you’ll acquire the essential skills needed to work with a range of tools and databases to design, deploy, and manage enterprise data warehouses, such as building data pipelines with Apache Airflow and Kafka to ETL data.

Requirements: This program does not require prior data engineering or programming experience or a specific educational background.

4. Meta Database Engineer Professional Certificate

Meta’s Database Engineer Professional Certificate is a flexible online certificate program that prepares learners for entry-level database engineering roles in six months or less. Designed for beginners, you’ll learn the key skills required to create, manage, and manipulate databases, along with programming languages and software such as SQL, Python, and Django. 

Requirements: This program does not require prior data engineering or programming experience or a specific educational background.

5. DeepLearning.AI Data Engineer Professional Certificate

DeepLearning.AI's Data Engineer Professional Certificate is a flexible online program designed to equip learners with practical and effective data engineering skills. Through this program, you'll gain hands-on experience with industry-standard tools like AWS Cloud, SQL, Apache Flink, Spark, and PySpark. It focuses on core intermediate data engineering skills such as feature engineering, networking in the cloud, DataOps, and Infrastructure as Code (IaC).

Requirements: This program requires intermediate Python skills.

Enhance your data engineer career today 

Data engineering is a popular, in-demand career field that can be exciting and fulfilling. If you enjoy building and tinkering with data systems to support business goals, you might be interested in one of the following Professional Certificates on Coursera:

To prepare for a career as a data engineer, explore the IBM Data Engineering Professional Certificate. In as little as six months, you can master practical data engineering skills and knowledge, such as NoSQL, Hadoop, and creating, designing, and managing relational databases.

To build on your existing data knowledge, take the DeepLearning.AI Data Engineer Professional Certificate. Learn the principles of good data architecture, explore frameworks for approaching data engineering projects, and build your skills in the five stages of the data engineering lifecycle.

To prepare for Google Cloud Certification, enroll in the Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate. Explore big data and machine learning products in Google Cloud, use Cloud SQL and Dataproc to migrate workloads to Google Cloud, and employ BigQuery for interactive data analysis.