Data Scientist
$114,848
avg. annual salary
Data Scientist
pays more on average
Data Engineer
$111,785
avg. annual salary
As organizations increasingly rely on data to drive decision-making, the roles of Data Scientists and Data Engineers have become essential. While both professionals work with data, their responsibilities and skill sets differ significantly. In this comprehensive salary comparison, we'll explore the average salaries, benefits, and career trajectories for Data Scientists and Data Engineers in 2025. Understanding these differences can help you make informed decisions whether you're considering a career in data or looking to hire the right talent for your organization. Let's delve into the specifics to find out how these two roles stack up against each other.
Salary by Experience Level
starting salary
starting salary
avg. difference (3%)
Average Salaries
In 2025, the average salary for a Data Scientist is estimated to be around $120,000, while a Data Engineer earns about $115,000. This slight difference reflects the diverse skill sets and responsibilities of each role.
Data Scientists often focus on analytical tasks, statistical modeling, and deriving insights from data, which can command a premium in specialized industries. Meanwhile, Data Engineers are crucial for building and maintaining the infrastructure necessary for data storage and processing, which is equally vital for any data-driven organization.
Salary by Experience Level
Both Data Scientists and Data Engineers experience a significant salary increase as they gain more experience. Entry-level Data Scientists can expect to earn between $85,000 and $95,000, whereas Entry-level Data Engineers typically earn between $80,000 and $90,000.
Mid-level professionals (3-5 years of experience) can see salaries ranging from $110,000 to $130,000 for Data Scientists and $105,000 to $125,000 for Data Engineers. Senior-level positions can command salaries of $150,000 or more, depending on the industry and geographical location.
Benefits and Perks
Both Data Scientists and Data Engineers enjoy competitive benefits packages, which may include health insurance, retirement plans, and bonuses. Companies often provide opportunities for skill development, such as training and workshops.
Flexible working hours and remote work options are common, especially in tech companies. However, Data Scientists may have access to additional perks, like participation in conferences or research grants, given their more analytical role.
Career Path and Growth Opportunities
Data Scientists often advance to roles like Lead Data Scientist or Data Science Manager, with opportunities to specialize in fields such as machine learning, artificial intelligence, or data visualization. On the other hand, Data Engineers can progress to Senior Data Engineer or Data Architect roles, focusing more on system design and architecture.
Both career paths offer substantial growth potential, with many companies investing heavily in data initiatives to drive business success.
Key Takeaways
When considering a career in data, it's essential to weigh the differences in salary, benefits, and career trajectories. Data Scientists typically command higher salaries, but Data Engineers are equally valued for their technical skills.
Both paths offer compelling opportunities for growth and development, making them attractive options in today's job market.
Detailed Comparison: Data Scientist vs Data Engineer (2025)
Below is a focused, numbers-driven comparison to help pick the best career or negotiation points.
- •Typical U.S. base salary ranges (2025):
- •Data Scientist: Entry $95K–$120K; Mid $120K–$160K; Senior $160K–$220K; Staff/Principal $220K–$300K+.
- •Data Engineer: Entry $90K–$120K; Mid $120K–$170K; Senior $150K–$230K; Staff/Principal $220K–$320K+.
- •Total compensation and industry effects:
- •Tech giants and late-stage AI startups often add 30%–80% in equity/bonuses. Example: a mid-level DS in NYC might have $140K base and $80K equity/bonus (total ≈ $220K).
- •Finance and ad tech commonly pay a 10%–25% premium; healthcare and nonprofit roles trend toward the lower half of ranges.
- •Skill-driven uplifts (approximate):
- •For Data Engineers: cloud + Spark + orchestration (Airflow/Kubernetes) → +8%–12% on base.
- •For Data Scientists: production ML + MLOps + model interpretability → +10%–15%.
- •Geography: SF/Seattle/NY/Remote-first firms typically add 20%–30% above national median; smaller markets can be 10%–20% below.
Actionable takeaways:
- •If you want higher immediate cash, target senior DE roles in cloud/infra or DS roles in finance/AI startups.
- •If equity matters, prioritize FAANG/Series C+ startups and document impact (metrics, revenue, latency) when negotiating.