Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

Reference: https://cloud.google.com/certification/data-engineer

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Machine Learning
• Data Processing Architectures
• Additional Resources
• Compute and Storage Fundamentals
• Scaling Data Analytics
• Introducing Google Cloud Platform ‘
• Data Analytics on the Cloud

Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

• Provisioning resources
• Data acquisition and import
• Validating a migration
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Lifecycle management of data
• Monitoring pipelines
• Building and operationalizing storage systems
• Integrating with new data sources
• Building and operationalizing processing infrastructure
• Storage costs and performance
• Transformation
• Building and operationalizing pipelines
• Batch and streaming
• Awareness of current state and how to migrate a design to a future state
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Data cleansing
• Adjusting pipelines
• Building and operationalizing data processing systems
• Testing and quality control