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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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

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Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.

Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems24%- Data processing and transformation
  • 1. Using Dataproc, Dataflow, and BigQuery SQL
    • 2. ETL/ELT pipeline design
      - Data ingestion and integration
      • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
        • 2. Streaming ingestion (Pub/Sub, Dataflow)
          Topic 2: Operationalizing machine learning models26%- ML pipeline integration
          • 1. Vertex AI pipeline deployment
            • 2. Feature engineering and feature stores
              - Model deployment and monitoring
              • 1. Model monitoring and drift detection
                • 2. Online vs batch prediction
                  Topic 3: Ensuring solution quality28%- Security and governance
                  • 1. IAM and access control in GCP
                    • 2. Data governance and compliance
                      - Reliability and performance
                      • 1. Fault tolerance and recovery strategies
                        • 2. Monitoring pipelines and workloads
                          Topic 4: Designing data processing systems22%- Batch and streaming data processing design
                          • 1. Event-driven vs batch architectures
                            • 2. Latency, throughput, and consistency trade-offs
                              - Data architecture and storage design
                              • 1. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                                • 2. Designing scalable and cost-effective data models

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