Google Cloud Certified - Professional Data Engineer

Professional Data Engineers enable organizations to derive value from data using Google Cloud technologies. With a strong understanding of data engineering principles and Google Cloud services, they design, build, operationalize, secure, and monitor data processing systems to support analytics and business intelligence needs.

The Google Cloud Certified – Professional Data Engineer certification exam assesses your ability to:

  • Design data processing systems

  • Build and operationalize data pipelines

  • Operationalize machine learning models

  • Ensure solution quality, security, and compliance

  • Monitor, optimize, and maintain data solutions

About this certification exam

  • Length: 2 hours

  • Registration fee: $200 (plus tax where applicable)

  • Languages: English, Japanese

  • Exam format: Multiple choice and multiple select, taken remotely or in person at a test center

Exam delivery method:

a. Online-proctored exam from a remote location
b. Onsite-proctored exam at a testing center

Prerequisites:

None

Recommended experience:

3+ years of industry experience, including 1+ years designing and managing data solutions using Google Cloud

Certification Renewal / Recertification:

Candidates must recertify to maintain their certification status. All Google Cloud certifications are valid for two years from the date of certification. Recertification is achieved by retaking the exam during the eligibility period and earning a passing score. You may attempt recertification starting 60 days before your certification expiration date.

For Google Cloud Certified Professional Data Engineer, the role reflects evolving demands in data-driven cloud environments:

  • Increased reliance on cloud-based data platforms and analytics solutions

  • Strong focus on designing, building, and maintaining scalable, secure, and reliable data pipelines

  • Core skills include data engineering, big data processing, machine learning support, security, and automation

  • Working with multiple Google Cloud data services, understanding their use cases, and ensuring optimal performance

  • Data Engineers must decide when to build custom data solutions or leverage managed services to improve efficiency, scalability, and cost optimization