Professional-Data-Engineer · ● Active · Professional · Google Cloud
Exam facts
| Field | Value |
|---|---|
| Cost | $200 USD |
| Duration | 2 hours (120 minutes) |
| Questions | ~60 |
| Passing | ~70% correct |
| Format | Multiple choice / Multiple select (scenario-based) |
| Delivery | Kryterion Webassessor (online or test centre) |
| Languages | English |
| Valid | 2 years |
| Renewal | Retake exam ($100 USD) |
| Prerequisites | Recommended: 3+ years industry experience, 1+ year with Google Cloud |
| Released | 2017 |
| Retiring | N/A |
Vendor source — cloud.google.com/learn/certification/data-engineer ↗ Official exam guide — Exam guide ↗
About
The Google Cloud Professional Data Engineer certification validates expertise in designing, building, and operationalizing data processing systems on Google Cloud Platform. This is one of Google Cloud's most popular certifications for data engineering roles, focusing on practical implementation of data pipelines, machine learning integration, and cost optimization. Holders demonstrate proficiency with BigQuery, Dataflow, Pub/Sub, Cloud Composer, and related GCP data services.
Domain context — Cloud
Hyperscale public cloud (GCP). Part of the Google Cloud certification track for cloud specialists.
Read full deep dive — Google Cloud →
Topics covered
Exam Domain Distribution:
- Designing Data Processing Systems (~25%) – Architect scalable, reliable data pipelines; evaluate batch vs. streaming; select appropriate GCP services; design for cost and performance
- Building and Operationalizing Data Processing Systems (~35%) – Implement pipelines using BigQuery, Dataflow, Pub/Sub, Cloud Composer; apply data quality practices; manage infrastructure
- Operationalizing Machine Learning Models (~15%) – Integrate ML workflows; manage feature engineering; deploy and monitor ML models on GCP
- Ensuring Solution Quality (~25%) – Monitor and troubleshoot; optimize costs; implement security and compliance; maintain data governance
Key Technologies Covered:
- BigQuery (data warehouse, ML integration)
- Apache Beam / Cloud Dataflow (stream and batch processing)
- Cloud Pub/Sub (event messaging)
- Cloud Composer (workflow orchestration)
- Dataproc (Spark/Hadoop managed service)
- Bigtable, Datastore, Firestore (NoSQL databases)
- Cloud Data Loss Prevention (DLP)
- Cloud KMS, CMEK (encryption)
- Datastream, Dataprep, Dataplex, Data Catalog
- Analytics Hub
- Looker (BI and analytics)
Common job-ready skills
- Design scalable, fault-tolerant data pipelines on GCP
- Optimize BigQuery performance and costs
- Implement real-time streaming with Pub/Sub and Dataflow
- Orchestrate complex data workflows with Cloud Composer
- Integrate machine learning models into data pipelines
- Implement data security, governance, and compliance
- Monitor, debug, and optimize data systems
- Select appropriate GCP services for use cases
Recommended courses
Google Cloud Skills Boost (official):
- Preparing for the Professional Data Engineer Exam
- Data Engineering with Google Cloud
- BigQuery for Data Analysis and Visualization
- Serverless Data Processing with Dataflow
Coursera:
Pluralsight:
- Google Cloud Professional Data Engineer Path (on-demand labs and courses)
Practice exams
- Official Google Cloud Practice Exam – Available free on Google Cloud Certification website (20 questions, no time limit)
- ExamTopics – Community practice questions and explanations
- CertificationPractice.com – Professional Data Engineer Practice Tests
Books
-
Official Google Cloud Certified Professional Data Engineer Study Guide by Dan Sullivan (2021)
- Publisher: Sybex
- ISBN: 978-1-119-61843-0
- Amazon ↗
-
Google Cloud Certified Professional Data Engineer Certification Guide by Marian Marinescu (2023)
- Publisher: Packt Publishing
- ISBN: 978-1-836641-31-5
- O'Reilly ↗
-
Google Cloud Certified Professional Cloud Developer Exam Guide by Sebastian Moreno (2023)
- Publisher: Packt Publishing
- ISBN: 978-1-800560-99-4
- O'Reilly ↗
Job titles
- Data Engineer
- Senior Data Engineer
- Principal Data Engineer
- Cloud Data Engineer
- BigQuery Engineer
- Data Pipeline Architect
- Analytics Engineer
Salary
United States (USD)
| Level | Low | Median | High |
|---|---|---|---|
| Entry | $95,000 | $115,000 | $135,000 |
| Mid | $125,000 | $145,000 | $165,000 |
| Senior | $160,000 | $180,000 | $210,000 |
South Africa (ZAR) (× 18)
| Level | Low | Median | High |
|---|---|---|---|
| Entry | R1,710,000 | R2,070,000 | R2,430,000 |
| Mid | R2,250,000 | R2,610,000 | R2,970,000 |
| Senior | R2,880,000 | R3,240,000 | R3,780,000 |
United Kingdom (GBP)
| Level | Low | Median | High |
|---|---|---|---|
| Entry | £75,000 | £90,000 | £105,000 |
| Mid | £100,000 | £115,000 | £130,000 |
| Senior | £130,000 | £150,000 | £175,000 |
European Union (EUR)
| Level | Low | Median | High |
|---|---|---|---|
| Entry | €80,000 | €98,000 | €115,000 |
| Mid | €110,000 | €130,000 | €155,000 |
| Senior | €150,000 | €175,000 | €210,000 |
Australia (AUD)
| Level | Low | Median | High |
|---|---|---|---|
| Entry | $155,000 | $185,000 | $215,000 |
| Mid | $205,000 | $235,000 | $270,000 |
| Senior | $270,000 | $310,000 | $360,000 |
Skills validated
- Design scalable, fault-tolerant data pipelines and data processing systems
- Implement real-time and batch processing solutions with appropriate GCP services
- Optimize data warehouse design and BigQuery performance
- Implement data security, encryption, and compliance controls
- Orchestrate multi-step data workflows using Cloud Composer
- Integrate machine learning models into production data pipelines
- Monitor system performance, diagnose issues, and optimize costs
- Select optimal GCP services for specific data engineering requirements
Related certs
- Google Cloud Associate Cloud Engineer
- Google Cloud Professional Cloud Architect
- Google Cloud Professional Machine Learning Engineer
- AWS Certified Data Analytics – Specialty
- Databricks Lakehouse Engineer Associate