Choose a credential for the work you want to do
Choose a data engineering credential based on the platform where you will build pipelines. SQL, data modeling, orchestration and recovery skills matter more than collecting exams across multiple clouds.
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Compare credentialsFollow the roadmap, choose relevant learning resources, build a project, then test your understanding with interview practice.
Explore free Data Engineer courses and a suggested learning order
Prepare your Data Engineer resume with keywords and evidence
Choose AWS Data Engineer Associate for AWS pipelines or Google Professional Data Engineer for Google Cloud data systems. If you are undecided about the platform, build a small pipeline with tests before paying for either.
Costs are listed in US dollars before local taxes and currency conversion; discounts, bundles and retakes can change the final price. Difficulty and “worth doing” verdicts are our editorial assessments, not provider guarantees. Recommended experience is not a formal prerequisite unless stated.
Scroll horizontally to compare all columns. Follow an exam link for its current terms.
| Provider | Exam / course name | Cost | Difficulty | Validity | Target experience | Free training options | Worth doing? |
|---|---|---|---|---|---|---|---|
| AWS | AWS Certified Data Engineer – AssociateProfessional certification | US$150 exam | Intermediate | 3 years Renewal policy | Provider targets 2–3 years in data engineering/architecture and 1–2 years using AWS. | AWS Glue documentationFree documentation for ingestion and transformation, plus the exam guide. Provisioned cloud resources may cost money. | Useful for established AWS data work |
| Google Cloud | Professional Data EngineerProfessional certification | US$200 standard exam | Advanced | 2 years via standard exam; alternative renewal routes have their own terms Renewal policy | Provider recommends 3+ industry years, including 1+ year designing and managing Google Cloud solutions. | BigQuery documentationFree documentation and the provider's exam guide/sample questions; training labs may need paid access. | Useful for Google Cloud data platform roles |
Useful for established AWS data work
Worth doing when: Worth considering if your target work uses AWS ingestion, transformation, orchestration and data governance. Use the syllabus to test the completeness of a pipeline project.
Skip or delay when: Do not interpret Associate as a beginner course. If SQL and data modeling are new, use free learning and a practical project first.
Useful for Google Cloud data platform roles
Worth doing when: Worth considering when you already make decisions about Google Cloud data processing, storage and analytics. The exam can support a platform-specific career move.
Skip or delay when: Skip if the jobs you want use a different stack. It does not replace evidence that you can design models, debug pipelines and control costs.
Ingest a changing dataset, validate its schema, model useful tables and implement a safe backfill. Show how you handle duplicates, late data, access controls and cost.
Explain the problem, your decision, your test and the result. A badge can support that story; it does not establish job readiness or guarantee a salary.
