Compare · Updated 16 September 2026
Cloud or DevOps Engineer vs Data Scientist: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Data Scientist pays more at mid-career: a median of $140,000 against $134,050 for Cloud or DevOps Engineer, about 4% higher. Cloud or DevOps Engineer is faster to enter: the quickest verified route takes about 12 months versus 12 for Data Scientist. Job growth favours Data Scientist (35% projected over ten years, BLS 2025-35, versus 8%).
| Cloud / DevOps Engineer | Data Scientist | |
|---|---|---|
| Entry median | $95,000 | $110,000 |
| Mid-career median | $134,050 | $140,000 |
| Senior median | $170,500 | $180,000 |
| Top end | $300,000 | $199,130 |
| Roadmap hours | 850 | 890 |
| Fastest way in | Cloud support engineer at a provider or MSP (12 mo) | Domain expert converting (12 mo) |
| Cheapest way in | $300 | $0 |
| Time to first job | 12–30 months | 18–36 months |
| Degree | No degree is legally required and this is one of the few technology fields where certifications carry genuine screening weight, so AWS Certified Solutions Architect - Associate plus a public Terraform repository substitutes for one at most employers; large enterprises and federal contractors still list a bachelor's degree as a preference, and a security clearance moves you past that filter entirely. | No degree is legally required, but a quantitative master's or PhD is a hard filter in pharma, biostatistics, clinical research and quantitative finance; product analytics teams at technology companies and most mid-size employers are where demonstrated skill substitutes for the credential. |
| 10-year growth | 8% | 35% |
| Openings per year | 23,000 | 24,800 |
| Automation exposure | medium | medium |
| Key certification | AWS Certified Solutions Architect - Associate (SAA-C03) | IBM Data Science Professional Certificate |
| Tools | Terraform, Docker, Kubernetes, AWS (EC2, S3, VPC, IAM, RDS, EKS, Lambda), GitHub Actions | Python, SQL (Snowflake, BigQuery, Databricks, Redshift), Jupyter / VS Code, scikit-learn, XGBoost / LightGBM |
Salary figures checked September 2026 (Cloud or DevOps Engineer) and September 2026 (Data Scientist). Sources are listed on each career page.
What a Cloud or DevOps Engineer does
A Cloud or DevOps Engineer is an engineer who builds and runs the platform other developers deploy onto: infrastructure defined in code with Terraform, containers and Kubernetes, continuous integration and delivery pipelines, observability, cloud identity and cost control, and the on-call response when any of it breaks.
A cloud or DevOps engineer builds and runs the platform everyone else deploys onto. That means infrastructure defined in code (Terraform), containers and orchestration (Docker, Kubernetes), CI/CD pipelines, observability, cost control, identity and access management, and the incident response that happens when any of it breaks at 3am. The job titles overlap heavily: cloud engineer, platform engineer, site reliability engineer, infrastructure engineer, DevOps engineer. The common thread is that your users are other engineers, and your product is the speed and safety with which they ship.
- Certifications genuinely carry weight here, unlike in application development, which gives a non-degree candidate a clear ladder
- Transfers directly from IT support, networking and military technical backgrounds
- Demand exists in every industry, not only at software companies, so you are not tied to a tech hub
What a Data Scientist does
A Data Scientist is a specialist who estimates what would happen rather than reporting what already did, by designing and reading out A/B tests, building forecasting and propensity models, and using causal inference when a clean experiment is impossible.
A data scientist sits between the analyst and the engineer. Where an analyst reports what happened, a data scientist estimates what would happen: designs and reads out A/B tests, builds forecasting and propensity models, does causal inference when a clean experiment is impossible, and builds the metric frameworks the rest of the company argues over. In practice the job splits into two archetypes. Product / decision science - heavy on SQL, experimentation, causal methods and stakeholder influence, common at consumer tech, marketplaces and fintech. Modelling / applied science - heavy on Python, scikit-learn, feature engineering and model evaluation, common in insurance, credit risk, healthcare, pricing and demand forecasting. Read the job description carefully, because the interview loops for the two are almost entirely different.
- BLS median of $120,230 with 35% projected growth to 2035 - one of the strongest wage-and-growth combinations in the US labour market.
- Genuine intellectual variety: the same week can involve causal inference, forecasting and a metric argument.
- Portable across industries; the statistics travels even when the domain does not.
How to choose between Cloud or DevOps Engineer and Data Scientist
- Pick Cloud or DevOps Engineer if almost nobody is hired straight into a Cloud or DevOps Engineer title with no professional experience, because the role carries production access; the route that produces most hires is an adjacent ticket-based job first - help desk, network operations centre, cloud support at a provider or managed service provider, at $45,000 to $65,000 - followed by an internal transfer in 18 to 30 months, usually with the employer paying for the certifications.
- Pick Data Scientist if the route that produces the most career-changer hires is analyst first and Data Scientist second: 18 to 30 months of production experience substitutes for a graduate degree in a way no certificate does, and you are paid throughout. Going direct from zero means competing with master's graduates for the same junior slot.
The natural next moves are Site Reliability Engineer, security engineering, cloud architecture and back-end software engineering. Site Reliability Engineer pays similarly but demands stronger coding; cloud security pays a premium and adds compliance work; architecture roles trade the pager for design reviews and stakeholder management, with Levels.fyi solution architect reports running $167,000 at the median and $345,700 at the 90th percentile. No degree bar changes on any of these moves. The natural next moves are machine learning engineer and AI engineer, which have absorbed most of the production modelling work and pay more, plus analytics and data science management. Both engineering moves raise the software bar (testing, services, deployment) rather than the statistics bar, and neither requires a graduate degree.
Cloud or DevOps Engineer vs Data Scientist FAQ
Which pays more, Cloud or DevOps Engineer or Data Scientist?
At mid-career the median is $134,050 for a Cloud or DevOps Engineer and $140,000 for a Data Scientist; at senior level $170,500 versus $180,000. Entry medians are $95,000 and $110,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Cloud or DevOps Engineer or a Data Scientist?
The quickest verified route into Cloud or DevOps Engineer is Cloud support engineer at a provider or MSP at about 12 months; for Data Scientist it is Domain expert converting at about 12 months. Our full roadmaps run 850 and 890 study hours respectively.
Which is harder to automate, Cloud or DevOps Engineer or Data Scientist?
We rate automation exposure medium for Cloud or DevOps Engineer and medium for Data Scientist. Infrastructure as code and AI assistants have already eliminated manual provisioning, which used to be the entry-level work, and that is one reason the ladder into this field now starts in help desk or cloud support rather than in junior system administration. What does not automate is judgment about failure: deciding that a release is not going out, finding the cause of a 503 at 2am, and designing a system whose blast radius is small enough to survive a mistake. Large language models already do exploratory analysis, boilerplate feature engineering and first-draft modelling code well, which erodes the junior end of Data Scientist work; the US Bureau of Labor Statistics cites the integration of AI into business workflows as a reason the occupation is projected to grow 35 percent to 2035. Experiment design, causal inference, spotting an artefact and persuading an organisation to act are not close to automated.
Do I need a certification for Cloud or DevOps Engineer or Data Scientist?
No certification is required, but they matter more here than in any other software career because recruiters screen on them. The one with the most screening value for a first cloud role is AWS Certified Solutions Architect - Associate at $150, realistically $165 to $500 all in with a course, about 80 to 150 hours of study; Microsoft AZ-104 at $165 and Google Associate Cloud Engineer at $125 are the equivalents in those ecosystems. The Certified Kubernetes Administrator at $445, including one free retake and two Killer.sh simulator sessions, carries the most signal with experienced interviewers because it is two hours at a live terminal rather than multiple choice, and HashiCorp Terraform Associate at $70.50 is the cheapest credible addition. AWS Certified Cloud Practitioner at $100 is an orientation course, not a hiring credential. No certification is required or expected for a Data Scientist job, and none of them substitutes for a degree or production experience. The most useful item on the list is the Machine Learning Specialization from Stanford Online and DeepLearning.AI at $49 a month for about two months and 95 hours, which is a curriculum rather than a credential. The Databricks Certified Machine Learning Associate at $200 is the only proctored exam here and it only matters if your target employers run Databricks; the DataCamp certifications carry little weight with hiring managers.