Which data engineering certifications employers actually screen for — AWS DEA-C01, DP-700, Google, Databricks and Snowflake — mapped to the modern data stack.
Half of the data engineering certifications you will find recommended online no longer exist. AWS retired its Data Analytics Specialty in April 2024 and its Database Specialty the same month. Microsoft retired DP-203 — for years the Azure data engineering exam — on 31 March 2025. If a list still ranks those, it was written for a market that has moved on. The current shelf is smaller, newer, and more tightly mapped to the modern data stack: cloud-native pipelines, lakehouse platforms, and warehouse-centric transformation.
This article ranks what is actually earnable in 2026 for data engineering roles specifically — not data analytics, not database administration, not data science. The distinction matters because data engineering interviews test pipeline design, distributed processing (Apache Spark above all), orchestration and data modelling, and the certifications worth your money are the ones that certify exactly that.
Three criteria, in order:
Demand context, with sources: Robert Half's 2026 US Salary Guide projects technology salaries up about 1.6% year on year and lists midpoint salaries of $153,750 for data scientists and $117,250 for data analysts — the roles that bracket data engineering in most org charts. Pay for any individual varies widely by location, experience and company; no certification carries a guaranteed salary, and you should treat any page quoting one universal "data engineer cert salary" number with suspicion.
The strongest general-purpose pick, for a blunt reason: AWS remains the most common cloud in data engineering job adverts, and this is its current, purpose-built data engineering credential — launched in 2024 as the effective replacement for the retired Data Analytics Specialty.
The verifiable facts as of 2026: 65 questions, 130 minutes, multiple choice and multiple response, $150 USD (varies by country and region), passing score 720 on AWS's 100–1,000 scale, valid three years. There are no prerequisites — AWS enforces none on any exam — but AWS suggests two to three years of data engineering experience and one to two years hands-on with AWS, and the suggestion is honest: the exam assumes you have actually run pipelines, not just watched videos about them.
What it certifies maps cleanly to the job: building and operationalising pipelines on AWS services, choosing data stores, transforming and cataloguing data, and applying security and governance to it. If you hold an active AWS certification already, remember the 50% discount voucher applies here too.
Choose DEA-C01 if: you are targeting the broadest slice of the job market, or your current employer is AWS-based. Look elsewhere first if: your target companies are visibly Databricks or Snowflake shops — platform match beats vendor prestige.
When you reach the readiness-testing stage, timed AWS Data Engineer Associate practice questions are most useful worked domain by domain: score a full set, identify your weakest exam domain, close it, and only then re-test — resist the urge to grind full papers on repeat.
Google Cloud's data engineering certification has a claim no rival quite matches: BigQuery, Dataflow and the surrounding analytics stack are the reason many companies choose Google Cloud at all, so the platform's data credential carries weight beyond Google Cloud's overall market share. Google Cloud certifications are valid for three years.
A transparency note this article owes you: our research pass did not independently verify the current exam fee, duration or question format for this certification, and Google's exam details change; take the specifics from the official page at cloud.google.com/learn/certification/data-engineer before booking. What is stable is its positioning — a professional-level (not associate) exam covering designing, building and operationalising data processing systems, with a reputation for scenario-heavy questions that assume real production judgement.
Choose it if: your market or target employer runs Google Cloud, or you already live in BigQuery. Sequence note: Google offers no data-specific associate rung, so many candidates warm up with the Associate Cloud Engineer first — reasonable, but not required. Once you are preparing in earnest, working through Google Professional Data Engineer practice questions under exam-like timing will surface whether your weakness is GCP service knowledge or data engineering fundamentals — they need different fixes.
The Azure story needs telling carefully, because the internet is full of DP-203 advice and DP-203 is gone. The Azure Data Engineer Associate certification retired on 31 March 2025; it cannot be earned, and Microsoft positions DP-700, the Fabric Data Engineer Associate, as the successor. The shift is more than a code change: DP-203 centred on Azure Synapse, while DP-700 centres on Microsoft Fabric — Microsoft's unified analytics platform — and expects SQL, PySpark and KQL.
Verified facts as of August 2026: the exam runs 100 minutes, is proctored via Pearson VUE (test centre or online), passes at 700 on Microsoft's 1,000-point scaled system (which is not 70% — a persistent misconception), and is priced by country rather than at a flat global figure. Its domains are implementing and managing an analytics solution, ingesting and transforming data, and monitoring and optimising an analytics solution. Like all Microsoft role-based certifications it expires after one year — but renewal is free, online, open-book and unproctored, so the short validity costs time rather than money.
Choose DP-700 if: you work in a Microsoft shop, or your region's enterprise market leans Azure. A caution: Fabric is newer than Synapse was, so check that your target employers have actually adopted it; a DP-700 in a Synapse-and-Databricks-on-Azure organisation lands slightly off-centre. Complete beginners on the Microsoft side can start with the never-expiring Azure Data Fundamentals — free DP-900 sample questions will show you quickly whether you are past that level already.
Databricks certification earns the fourth slot on ecosystem weight: the lakehouse platform and its Spark-based tooling sit at the centre of a large share of modern pipeline work, and Apache Spark fluency — which Databricks exams test in its native habitat — is among the most-requested data engineering skills anywhere. Databricks offers the data engineer track at Associate and Professional tiers.
Honesty about limits again: our research pass verified no current pricing, format or validity details for the Databricks exams, so this entry describes positioning, not specifications — pull the current details from databricks.com/learn/certification before planning. What can be said with confidence is strategic: a Databricks credential is the strongest platform-specific signal on this list for employers who run it, and close to irrelevant for employers who do not. It is also cloud-portable — Databricks runs on AWS, Azure and Google Cloud — which makes it a good second certification layered over a cloud credential rather than a first one instead.
Choose it if: job adverts you are reading name Databricks or heavy Spark work. Pair it with: whichever cloud certification matches those same adverts.
Snowflake's certification ladder starts with SnowPro Core and branches into advanced role tracks, including data engineering. The reasoning mirrors Databricks with one difference of emphasis: Snowflake shops concentrate in warehouse-centric, SQL-and-ELT-style stacks, so a SnowPro credential signals a slightly different flavour of data engineering — transformation, modelling and warehouse operations more than distributed processing.
As with Databricks, current exam fees, formats and validity were not verified in our research pass; treat snowflake.com/certifications as the source of record. Rank it fifth as a deliberate specialisation for candidates whose target market runs Snowflake, not as a general-purpose opener.
| Certification | Platform | Level | Verified cost (2026) | Validity | Best-fit reader |
|---|---|---|---|---|---|
| AWS DEA-C01 | AWS | Associate | $150 USD (varies by region) | 3 years | Broadest market coverage |
| Google Professional Data Engineer | Google Cloud | Professional | Confirm on official page | 3 years | BigQuery/GCP environments |
| Microsoft DP-700 | Azure / Fabric | Associate | Country-based pricing | 1 year, free renewal | Microsoft enterprise shops |
| Databricks Data Engineer | Databricks (any cloud) | Associate / Professional | Confirm on official page | Confirm on official page | Spark/lakehouse-heavy roles |
| SnowPro track | Snowflake | Core + specialisation | Confirm on official page | Confirm on official page | Warehouse/ELT-centric roles |
No universal winner is being crowned here, and that is not hedging: the same certificate is a door-opener at one company and a shrug at the next, entirely depending on the stack. Match the certification to the job adverts you are genuinely going to answer.
A realistic scenario makes the sequencing concrete. Suppose you are an analyst two years into a role, strong in SQL, moving towards data engineering:
If you are earlier in the journey than this scenario — no cloud exposure at all — start a rung lower and come back: our guide to choosing a certification after AWS Cloud Practitioner covers the foundational-to-associate step, and the wider cloud certification comparison across AWS, Azure and Google Cloud covers choosing an ecosystem in the first place. And if your interest is really the systems-design side of data platforms rather than pipeline building, the solutions architecture certification track is the neighbouring path — one sentence of overlap, different career.
Naming the exclusions guards you against stale advice elsewhere:
Google Professional Data Engineer vs AWS Data Engineer Associate — which is harder?
They sit at different tiers: Google's is professional-level and scenario-driven; AWS's is associate-level with a suggested two to three years of experience behind it. Neither publishes pass rates, so ignore difficulty percentages you see quoted. Pick by platform first; if truly platform-agnostic, the associate-level exam is the gentler entry.
Is DP-203 still worth anything on a CV?
An earned certification stays on your transcript, and holders kept validity per Microsoft's rules at the time — but it can no longer be earned or renewed. If you hold it, list it; if you were planning it, DP-700 is the exam that exists.
Do I need a cloud certification before Databricks or Snowflake ones?
Not formally. Practically, most data engineering jobs deploy those platforms inside a cloud account, so the cloud-plus-platform pairing reads as a complete story where a platform-only credential reads as half of one.
How much do data engineers earn?
Our verified sources give brackets rather than a data-engineer-specific figure: Robert Half's 2026 US guide midpoints put data scientists at $153,750 and data analysts at $117,250, with data engineering typically advertised between those markets. Location, experience and company size move the number more than any certification does — treat certificates as interview access, not salary triggers.
Strip the analysis to its actionable core: DEA-C01 for the widest market, DP-700 for Microsoft shops, the Google Professional Data Engineer for GCP environments, Databricks or SnowPro layered on top when the adverts demand them — and verify every fee and format on the provider's official page, because 2024–2026 retired more data certifications than any period before it. Then split your preparation deliberately: build one real pipeline for the interview, and use timed, domain-scored practice tests to prove the exam is ready to be booked rather than hoped about.
Exam facts in this guide were checked against official certification-provider pages on . Fees, exam codes and policies change — confirm on the provider’s own site before you book.
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