The best data engineering certifications in 2026 are AWS Certified Data Engineer – Associate (widest job market reach), Microsoft Fabric DP-700 (strongest in enterprise), and Databricks DE Associate (fastest-growing demand). GCP Professional Data Engineer, Snowflake SnowPro Core, and dbt Analytics Engineering round out the top six. Start with the one that matches your target job market — not the one with the most prestige.
- Compare all major data engineering certifications side-by-side (cost, difficulty, ROI, time)
- Understand which certification matches your career stage and tech stack
- Follow a recommended certification path based on your career goal
- Avoid common certification mistakes that waste time and money
- Add certifications to your resume and LinkedIn for maximum impact
Quick Answers
What is the best data engineering certification in 2026?
AWS Certified Data Engineer – Associate (DEA-C01) has the broadest market impact. AWS services appear in more data engineering job postings than any other cloud provider. If your target companies use Microsoft or Databricks, those platform-specific certs are equally valuable.
Which data engineering certification should I get first?
Match the certification to the job postings you're targeting. If 60%+ mention AWS services — get AWS DEA-C01. If they mention Azure or Fabric — get DP-700. If they mention Databricks — get Databricks DE Associate. When in doubt, AWS DEA-C01 has the widest applicability.
Are data engineering certifications worth it?
Yes, especially for career changers and engineers without a CS degree. Certifications provide a verifiable signal to hiring managers and recruiters. They don't replace experience, but they get your resume past the initial screen — particularly at large enterprises with formal credential requirements.
How many data engineering certifications do I need?
One or two. One cloud cert (AWS, Azure, or GCP) plus one platform cert (Databricks or Snowflake) covers the broadest set of job requirements. More than two shows diminishing returns — hiring managers care more about hands-on experience than a collection of badges.
There are over a dozen data engineering certifications on the market in 2026, and the landscape changes fast — Microsoft retired DP-203 in March 2025 and replaced it with DP-700, Snowflake is launching COF-C03 in February 2026, and Databricks is expanding its certification program. Picking the wrong one means wasting $150–$200 and weeks of study time on a credential that doesn't move the needle.
This guide ranks the six certifications that actually matter for data engineers, based on job market demand, employer recognition, and career ROI — not marketing hype.
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Learn how Careery can help youWhy Certifications Matter for Data Engineers (and When They Don't)
- Data Engineering Certification
A vendor-issued credential that validates technical knowledge in cloud data services, data processing platforms, or data tooling through a proctored exam. Major issuers include AWS, Microsoft, Google, Databricks, and Snowflake.
Certifications serve a specific purpose in the hiring pipeline: they are a verifiable signal that passes automated filters. When a recruiter searches LinkedIn for "AWS data engineer certified" or an ATS scans resumes for "Databricks certification," the credential either exists or it doesn't.
When Certifications Help Most
- Career changers — moving from software engineering, data analysis, or other fields into data engineering. A certification supplements project experience with a recognized credential.
- No CS degree — provides formal validation that hiring managers can point to during the evaluation process.
- Targeting specific platforms — AWS certification for AWS-heavy companies, DP-700 for Microsoft Fabric shops. The signal is most effective when it matches the employer's stack.
- Enterprise job applications — large organizations (banks, healthcare systems, government contractors) frequently list certifications as requirements, not just preferences.
When They Don't
- 10+ years of deep experience — at senior/staff level, your track record speaks louder than badges.
- Collecting without applying — certifications on a resume with no corresponding project experience look performative. One cert plus hands-on experience beats three certs with no projects.
- Wrong platform for your market — an AWS cert doesn't help if every company in your city runs on Azure.
Certifications are hiring signals, not substitutes for experience. They work best when they match the job market you're targeting and are backed by hands-on project work.
The Best Data Engineering Certifications Ranked
This ranking prioritizes career ROI — the combination of job market demand, employer recognition, exam quality, and long-term value. Every certification listed has been independently verified against the official certification pages as of February 2026.
#1: AWS Certified Data Engineer – Associate (DEA-C01)
Why it's #1: AWS dominates the cloud data engineering job market. More data engineering job postings reference AWS services (Glue, Redshift, Kinesis, S3) than any other cloud provider. The DEA-C01 exam specifically validates the services that data engineers use daily — not general cloud knowledge.
Exam domains:
- Data Ingestion and Transformation — 34%
- Data Store Management — 26%
- Data Operations and Support — 22%
- Data Security and Governance — 18%
Passing score: 720 out of 1,000 (scaled). The exam includes 15 unscored pilot questions mixed in with the 50 scored questions.
Study time: 4–8 weeks for working data engineers with AWS experience. Career changers may need 8–12 weeks.
Best for: Anyone targeting AWS-heavy data roles, which represent the largest share of the market.
For the full exam breakdown, domain-by-domain study plan, and registration walkthrough, see our dedicated guide: AWS Data Engineer Certification (DEA-C01): Complete Guide.
#2: Microsoft Fabric Data Engineer Associate (DP-700)
Why it's #2: Microsoft Fabric is replacing the legacy Azure data stack (Synapse, Data Factory, Power BI) with a unified analytics platform. Enterprise organizations — especially in finance, healthcare, and government — rely heavily on Microsoft tooling. The DP-700 replaced the retired DP-203 (Azure Data Engineer Associate) in 2025 and tests the new Fabric platform.
Exam domains:
- Implement and Manage an Analytics Solution — 30–35%
- Ingest and Transform Data — 30–35%
- Monitor and Optimize an Analytics Solution — 30–35%
Passing score: 700 out of 1,000. Microsoft does not publish the exact number of questions, but community reports indicate 40–60.
Study time: 4–6 weeks for engineers already working with Azure or Fabric. Longer if transitioning from AWS or GCP.
Important: The older Azure Data Engineer Associate (DP-203) was retired on March 31, 2025. If you see job postings mentioning "DP-203," they likely haven't updated their requirements — DP-700 is the current credential.
Best for: Data engineers in Microsoft-heavy enterprises, anyone working with Fabric, OneLake, or Dataflows Gen2.
For the full exam breakdown, DP-203 vs DP-700 comparison, and study plan, see our guide: Azure Data Engineer Certification (DP-700): Complete Guide.
#3: Databricks Certified Data Engineer Associate
Why it's #3: Databricks adoption is surging. The lakehouse architecture (Delta Lake, Unity Catalog, Lakeflow) is becoming the default for organizations that want a unified data platform without vendor lock-in to a single cloud. The exam tests practical skills that transfer directly to daily work.
Exam domains:
- Databricks Lakehouse Platform — 10%
- ELT with Spark SQL and Python — 30%
- Incremental Data Processing — 31%
- Production Pipelines — 18%
- Data Governance — 11%
Passing criteria: Pass/Fail — Databricks does not publish the exact score threshold. Community consensus suggests approximately 70%.
Study time: 4–6 weeks for engineers using Databricks daily. 6–10 weeks if learning from scratch.
Best for: Data engineers on Databricks-centric teams. Pairs exceptionally well with a cloud cert (AWS or Azure) since Databricks runs on top of those clouds.
For the full exam breakdown, study plan, and registration walkthrough, see our guide: Databricks Data Engineer Certification Guide.
#4: Google Cloud Professional Data Engineer
Why it's ranked here: The GCP Professional Data Engineer certification is respected for its difficulty — it's a Professional-level exam, not Associate-level like the others. It covers BigQuery, Dataflow, Pub/Sub, Cloud Composer, and the full GCP data stack. However, GCP holds a smaller share of the enterprise data engineering market compared to AWS and Azure.
Exam content areas:
- Designing data processing systems — ~22%
- Ingesting and processing data — ~25%
- Storing data — ~20%
- Preparing and using data for analysis — ~15%
- Maintaining and automating data workloads — ~18%
Passing criteria: Pass/Fail — Google does not disclose the passing score.
Study time: 6–10 weeks. The Professional-level designation means broader and deeper coverage than Associate exams.
Important note: Google lists 40–50 multiple choice and multiple select questions. No formal prerequisites, but Google recommends 3+ years of industry experience.
Best for: Data engineers at Google Cloud shops, startups that standardize on GCP, and anyone who wants a credential that signals deeper technical mastery (Professional vs Associate level).
GCP is the third-largest cloud provider behind AWS and Azure. If your target companies don't use GCP, this certification has less direct value — but it carries prestige and demonstrates strong fundamentals.
#5: Snowflake SnowPro Core (COF-C03)
Why it's ranked here: Snowflake is the dominant cloud data warehouse for companies that don't want to be locked into a single cloud provider. The SnowPro Core certification validates Snowflake-specific knowledge — architecture, virtual warehouses, data sharing, Time Travel, and security. It's particularly relevant for data engineers at companies that use Snowflake as their primary warehouse.
Important: The COF-C02 exam is being retired and replaced by COF-C03, launching February 16, 2026. The new version covers updated Snowflake features including AI Data Cloud capabilities, unstructured data handling, and Snowpark.
Exam domains (COF-C03):
- Snowflake AI Data Cloud Features and Architecture
- Account Access and Security
- Performance Concepts
- Data Loading and Unloading
- Data Transformations
- Data Protection and Data Sharing
Passing score: 750 out of 1,000 (75 correct out of 100).
Study time: 3–4 weeks for engineers using Snowflake daily. 4–6 weeks if new to the platform.
Best for: Data engineers at Snowflake-centric companies. Often paired with a cloud cert (AWS, Azure, or GCP) since Snowflake runs on all three.
#6: dbt Analytics Engineering Certification
Why it's ranked here: dbt (data build tool) has become the standard for SQL-based data transformation in the modern data stack. The certification tests dbt Core and dbt Cloud knowledge — models, tests, sources, documentation, and deployment. It's valuable for analytics engineers and data engineers who work heavily with the transformation layer.
Exam details:
- Question types: multiple-choice, fill-in-the-blank, matching, hotspot, and build-list
- Passing score: 65% or higher
- Covers dbt Core version 1.7
- Prerequisites: SQL proficiency and 6+ months of dbt experience recommended
Best for: Analytics engineers, data engineers focused on the transformation layer, and anyone working in the "modern data stack" (Fivetran/Airbyte + dbt + Snowflake/BigQuery).
The dbt certification is narrower in scope than cloud platform certs. It proves transformation-layer expertise but doesn't cover infrastructure, ingestion, or orchestration. Consider it a complement to a cloud cert, not a replacement.
Certification Comparison Table
AWS DEA-C01 offers the best cost-to-market-value ratio ($150, 3-year validity, widest job market reach). GCP Professional Data Engineer is the hardest exam and carries the most technical prestige. dbt is the most accessible entry point.
Which Certification Should You Get First?
The right first certification depends on one question: what cloud or platform do the jobs you're targeting use?
Check 20 Job Postings in Your Target Market
Search for "data engineer" on LinkedIn, Indeed, or your preferred job board. Look at the required and preferred qualifications sections. Count how many mention AWS, Azure/Fabric, GCP, Databricks, or Snowflake. The platform mentioned most often is your answer.
Match the Certification to the Market
- 60%+ mention AWS services (Glue, Redshift, S3, Kinesis): → AWS DEA-C01
- 60%+ mention Azure, Fabric, or Synapse: → Microsoft DP-700
- 60%+ mention Databricks, Spark, Delta Lake: → Databricks DE Associate
- 60%+ mention BigQuery, Dataflow, GCP: → GCP Professional Data Engineer
- 60%+ mention Snowflake: → Snowflake SnowPro Core
- Split across multiple platforms: → AWS DEA-C01 (broadest general value)
Plan Your Second Certification (Optional)
After your first cert, a complementary second cert broadens your profile:
- AWS + Databricks — covers the largest combined job market (Databricks runs on AWS for most teams)
- Azure + Databricks — strong for enterprise roles (Azure Databricks is a popular combo)
- Cloud cert + Snowflake — for warehouse-heavy roles regardless of cloud provider
- Cloud cert + dbt — for analytics engineering / modern data stack roles
Certification Mistakes to Avoid
- Getting a certification that doesn't match your target job market (e.g., AWS cert when every local company uses Azure)
- Collecting 3+ certifications without corresponding hands-on experience — hiring managers see through this
- Studying for an exam without hands-on practice — every major cert has a free tier or sandbox to build real projects
- Ignoring the retired DP-203 — if your study materials reference DP-203, they're outdated. Microsoft replaced it with DP-700 in 2025
Match the certification to the job market, not the other way around. One targeted cert with hands-on experience beats three mismatched certs.
Certification Path by Career Goal
Different career trajectories benefit from different certification sequences:
Career Changer → Data Engineer
First: One Cloud Certification
Pick the cloud your target employers use. AWS DEA-C01 is the safest default if you're unsure.
Then: Build 2–3 Portfolio Projects
Use the cloud services from your certification in real projects. An API-to-warehouse pipeline, a streaming pipeline, and a multi-source integration project demonstrate practical skills.
Optional: Add Databricks or dbt
If your target roles mention these tools, add the relevant certification. This shows breadth across the modern data stack.
Junior → Mid-Level Data Engineer
Focus on deepening your primary platform expertise:
- Already on AWS? → AWS DEA-C01 if you haven't already
- Using Databricks at work? → Databricks DE Associate to formalize your skills
- In a Microsoft shop? → DP-700 to demonstrate Fabric competency to your manager
Mid-Level → Senior / Staff
At this level, certifications matter less than impact. Consider:
- GCP Professional Data Engineer — the Professional-level designation signals depth, and multi-cloud knowledge is valued at the senior level
- Databricks DE Professional — when it becomes available, this validates advanced Spark and lakehouse skills
- Focus on architecture and leadership instead of collecting more Associate-level certs
For the complete data engineer career progression — from breaking in to reaching staff level — see our guide: How to Become a Data Engineer: Complete Career Guide.
Wondering how certifications affect compensation? Certified engineers tend to land roles at higher-paying companies. See the full breakdown: Data Engineer Salary Guide 2026.
How to Maximize Certification ROI
Earning the badge is step one. Making it drive career outcomes is step two.
Don't Just Collect Badges
A certification on a resume with no corresponding project experience looks performative. For every cert you earn:
- Build a project using the certified platform that you can discuss in interviews
- Update your resume with both the certification and the project that demonstrates it
- Add it to LinkedIn in the Certifications section with the verification URL
Time Your Certifications Strategically
- Before a job search — the certification gives recruiters a reason to shortlist you
- During onboarding at a new role — your employer may cover the cost and study time
- After completing a major project — the project experience makes the certification study faster
Resume and LinkedIn Best Practices
- List certifications in a dedicated "Certifications" section, not buried in education
- Include the credential ID and verification URL on LinkedIn
- Reference the certification in your headline if it's directly relevant: "Data Engineer | AWS Certified"
For specific guidance on listing certifications, optimizing for ATS, and writing data engineer resume bullets, see our Data Engineer Resume Guide.
A certification creates value when it's backed by hands-on experience and visible to the right audience. Earn it, build with it, and make it findable.
Key Takeaways
- 1AWS DEA-C01 is the best first certification for most data engineers — widest job market reach at $150
- 2Microsoft DP-700 replaced the retired DP-203 and is essential for Fabric/enterprise roles
- 3Databricks DE Associate is the fastest-growing certification, ideal for lakehouse-centric teams
- 4GCP Professional Data Engineer carries the most technical prestige (Professional-level exam)
- 5Match your certification to job postings — check 20 listings before deciding
- 6One or two certifications plus hands-on projects beats a collection of badges with no experience
- 7Time certifications strategically — before a job search or during onboarding at a new role
Frequently Asked Questions
What is the easiest data engineering certification?
The dbt Analytics Engineering Certification is the most accessible, requiring a 65% passing score and focusing on SQL-based transformation. Among cloud certifications, the Databricks DE Associate is the shortest (90 minutes, 45 questions) and has the narrowest scope. AWS DEA-C01 and SnowPro Core are moderate difficulty.
Do data engineering certifications expire?
Yes. AWS DEA-C01 is valid for 3 years. Microsoft DP-700 requires annual renewal (free renewal assessment). Databricks, GCP, Snowflake, and dbt certifications are valid for 2 years each. Plan for recertification as part of your professional development.
Can I get a data engineering certification with no experience?
Technically yes — none of the major certifications have formal prerequisites. However, the exams assume hands-on familiarity with the platforms. AWS recommends 2–3 years of experience, Google recommends 3+ years. With no experience, expect longer study times (8–12 weeks) and heavy use of free-tier labs and sandbox environments.
Is the GCP Professional Data Engineer certification harder than AWS?
Yes. GCP Professional Data Engineer is a Professional-level exam (broader scope, deeper questions) while AWS DEA-C01 is Associate-level. GCP also doesn't disclose the passing score. Most candidates report that GCP requires significantly more study time.
Should I get AWS or Azure data engineering certification?
Check your target job market. If job postings mention AWS services (Glue, Redshift, S3), get AWS DEA-C01. If they mention Azure, Fabric, or Synapse, get Microsoft DP-700. If you're unsure, AWS has the larger market share overall, but Azure dominates in enterprise sectors like finance and healthcare.
Is the Snowflake SnowPro Core worth it for data engineers?
Yes, if your target companies use Snowflake as their primary data warehouse. The SnowPro Core validates platform-specific knowledge that cloud certs don't cover (Time Travel, data sharing, virtual warehouse optimization). It pairs well with a cloud cert since Snowflake runs on AWS, Azure, and GCP.
What happened to the Azure DP-203 certification?
Microsoft retired DP-203 (Azure Data Engineer Associate) on March 31, 2025. It was replaced by DP-700 (Microsoft Fabric Data Engineer Associate), which focuses on the new Microsoft Fabric platform instead of legacy Azure Data Factory and Synapse. If you hold DP-203, it remains valid until its expiration date but cannot be renewed — you must take DP-700 instead.
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Sources & References
- AWS Certified Data Engineer – Associate (DEA-C01) — Amazon Web Services (2026)
- Microsoft Certified: Fabric Data Engineer Associate (DP-700) — Microsoft Learn (2026)
- Databricks Certified Data Engineer Associate — Databricks (2026)
- Professional Data Engineer Certification — Google Cloud (2026)
- SnowPro Core Certification (COF-C03) — Snowflake (2026)
- dbt Analytics Engineering Certification Exam — dbt Labs (2026)