Data Scientist
$124,861
avg. annual salary
Cloud Engineer
pays more on average
Cloud Engineer
$134,232
avg. annual salary
Choosing between a career as a Data Scientist or a Cloud Engineer? Both roles are in high demand and offer competitive salaries, but they focus on different skill sets and responsibilities. Data Scientists apply statistical analysis and machine learning to extract insights from data, while Cloud Engineers design and manage cloud computing systems. Understanding the salary disparities, benefits, and growth opportunities in these fields will help you make an informed career decision. This guide provides a thorough examination of what you can expect in terms of earnings, growth potential, and job satisfaction in both professions. Let’s dive into the details and help you find the right path for your future.
Salary by Experience Level
starting salary
starting salary
avg. difference (8%)
Salary Overview
As of 2025, the average salary for a Data Scientist is approximately $120,000, while a Cloud Engineer earns around $115,000. The salary range for Data Scientists typically falls between $100,000 and $150,000, whereas Cloud Engineers can expect a range of $95,000 to $145,000.
Keep in mind that salaries can vary greatly based on factors such as location, experience, and education.
Benefits and Perks
Both Data Scientists and Cloud Engineers often enjoy similar benefits, including health insurance, retirement plans, and paid time off. A significant aspect of many tech jobs is the opportunity for remote work, particularly for Cloud Engineers due to their focus on online systems.
Additionally, professional development opportunities such as training and certifications are common in both fields.
Career Path and Growth Potential
Data Scientists often progress to senior roles, such as Data Analytics Manager or Chief Data Officer, where strategic decision-making and leadership skills become crucial. On the other hand, Cloud Engineers can advance to positions like Cloud Architect or Director of Cloud Operations.
Both paths offer substantial growth opportunities, especially as companies continue to invest in data-driven strategies and cloud technologies.
Skills Required
Data Scientists typically need strong programming skills (in languages like Python and R), statistical analysis, and machine learning expertise. Cloud Engineers should be proficient in programming languages, cloud platforms (such as AWS or Azure), and system architecture.
Both roles require problem-solving abilities and good communication skills for presenting insights or working with cross-functional teams.
Job Market Demand
The demand for both Data Scientists and Cloud Engineers remains strong. According to industry reports, job openings for Data Scientists have increased by 30% over the past few years, while Cloud Engineer positions have surged due to businesses accelerating their digital transformation and cloud migration efforts.
Detailed Comparison: Salaries, Roles, and Progression
### Quick salary snapshot (US, 2025 estimates)
- •Entry: Data Scientist $75k–$95k; Cloud Engineer $85k–$105k
- •Mid (3–7 years): Data Scientist $110k–$145k; Cloud Engineer $120k–$160k
- •Senior/Lead: Data Scientist $150k–$200k+; Cloud Engineer $160k–$220k+
### Role differences that affect pay
- •Data Scientists focus on modeling, A/B testing, and feature engineering; companies pay a premium (5–15% higher) when you ship models to production or reduce churn by >5%.
- •Cloud Engineers own infrastructure, cost optimization, and reliability; mastering Kubernetes, Terraform, and cloud cost reduction (e.g., cut spend 20%) often yields bigger raises.
### Career progression
- •Data Scientists often move into ML engineering or product science.
- •Cloud Engineers progress to platform or architecture roles with larger equity opportunities.
Actionable takeaway: target skills that directly save money or drive revenue (e. g.
, deployment pipelines or cost reductions) to maximize salary growth.
Factors to Consider When Choosing Between Roles
### Key factors that influence compensation and fit
- •Location and remote work: salaries vary 15–40% by metro; remote-friendly companies may offer 5–10% less base but more flexibility.
- •Industry: finance and healthcare pay 10–30% above average for both roles.
- •Certification and tools: AWS/Azure/GCP certs and Terraform/Kubernetes for cloud; MLOps, PyTorch/TensorFlow, and model deployment experience for data science — certifications can add $5k–$15k.
- •On-call and hours: ~30–50% of cloud roles require on-call rotations, which often include 5–15% additional compensation or time-off.
- •Equity and bonuses: startups may offer 0.01%–0.5% equity; larger firms give 5–20% of base as bonus.
Actionable takeaway: list 3 must-have factors (pay, work-life, growth) and score each job offer against them before deciding.