- 1.Data Scientists earn slightly higher median salary ($128,078) than Data Engineers ($120,000-$135,000 range), but the gap narrows at senior levels (BLS 2024)
- 2.Data Engineers have better job security—every company needs data infrastructure, while ML/AI projects can be cut during downturns
- 3.Both roles show exceptional growth: Data Scientists +35%, Software Developers (includes Data Engineers) +25% through 2033
- 4.At Staff/Principal level, Data Engineers often out-earn Data Scientists due to critical infrastructure ownership
- 5.Skills overlap is significant—Python, SQL, and cloud platforms are essential for both roles
Salary Overview: Data Engineer vs Data Scientist
The data engineering vs data science salary debate is nuanced. While Data Scientists edge out Data Engineers in median pay ($128K vs $125K), the picture changes significantly by experience level, industry, and specialization. Understanding these dynamics is crucial for career planning.
Both roles have exploded in demand as organizations prioritize data-driven decision making. However, they serve different functions: Data Engineers build the pipelines and infrastructure that make data usable; Data Scientists analyze that data to extract insights and build predictive models.
Data Engineer vs Data Scientist: Head-to-Head
Comparing median compensation and job market for each role
Data Scientist
Median Annual Salary
Data Engineer
Median Annual Salary
Role Differences: What Each Actually Does
The salary comparison only makes sense with clear understanding of what each role involves. These aren't interchangeable titles—they require different skills, solve different problems, and have different day-to-day responsibilities.
Builds and maintains the data infrastructure. Designs ETL/ELT pipelines, data warehouses, and streaming systems. Ensures data quality, reliability, and accessibility. The 'plumber' who makes sure data flows correctly.
Key Skills
Common Jobs
- • Data Engineer
- • Analytics Engineer
- • Platform Engineer
- • ETL Developer
Extracts insights from data through statistical analysis and machine learning. Builds predictive models, runs experiments, and communicates findings to stakeholders. The 'analyst' who turns data into business value.
Key Skills
Common Jobs
- • Data Scientist
- • ML Engineer
- • Research Scientist
- • Applied Scientist
Data Engineer vs Data Scientist: Role Comparison
| Factor | Data Engineer | Data Scientist | Edge |
|---|---|---|---|
| Primary Focus | Infrastructure & pipelines | Analysis & modeling | — |
| Key Deliverables | Data pipelines, warehouses | Models, insights, reports | — |
| Math Requirements | Moderate | Heavy (statistics, linear algebra) | Engineer |
| Coding Depth | Deep (systems, optimization) | Moderate (scripting, libraries) | Engineer |
| Stakeholder Interaction | Moderate (technical teams) | High (business + technical) | Scientist |
| Job Security | Higher (always needed) | Variable (project-based) | Engineer |
| Entry Barrier | Moderate | Higher (often requires MS/PhD) | Engineer |
Source: Industry analysis
Salary by Experience Level
The salary gap between roles changes dramatically with experience. Data Scientists start slightly higher, but Data Engineers often catch up or surpass at senior levels due to the critical nature of infrastructure work.
Compensation by Experience Level
| Level | Data Engineer | Data Scientist | Difference |
|---|---|---|---|
| Entry (0-2 years) | $85,000-$105,000 | $90,000-$115,000 | DS +$10K |
| Mid (2-5 years) | $110,000-$140,000 | $115,000-$145,000 | DS +$5K |
| Senior (5-8 years) | $140,000-$175,000 | $140,000-$175,000 | Even |
| Staff (8-12 years) | $170,000-$220,000 | $165,000-$210,000 | DE +$5K |
| Principal (12+ years) | $200,000-$280,000 | $190,000-$260,000 | DE +$15K |
Source: Levels.fyi, Glassdoor 2024
Data Scientist Employment
Data Scientist roles projected to grow 35% from 2023-2033—nearly 9x the average job growth rate. This exceptional demand drives competitive compensation.
Salary by Industry
Industry choice significantly impacts compensation for both roles. Finance and tech pay premiums, while healthcare and government offer more modest salaries but better stability.
Median Salary by Industry
| Industry | Data Engineer | Data Scientist | Notes |
|---|---|---|---|
| Big Tech (FAANG) | $180,000-$250,000 | $175,000-$240,000 | TC can reach $400K+ |
| Finance/Trading | $160,000-$220,000 | $165,000-$230,000 | Quant roles pay more |
| Startups (funded) | $140,000-$180,000 | $135,000-$175,000 | Equity can add $50K+ |
| Enterprise Tech | $130,000-$165,000 | $125,000-$160,000 | Stable, less upside |
| Healthcare | $110,000-$145,000 | $105,000-$140,000 | Growing demand |
| Government/Nonprofit | $90,000-$120,000 | $85,000-$115,000 | Strong benefits |
Source: Glassdoor, LinkedIn Salary Insights 2024
Geographic Salary Variations
Location matters, though remote work has compressed geographic differentials. Both roles follow similar geographic patterns.
Salary by Metro Area
| Metro | Data Engineer | Data Scientist | COL Index |
|---|---|---|---|
| San Francisco Bay Area | $165,000 | $170,000 | 188 |
| Seattle | $155,000 | $160,000 | 159 |
| New York | $150,000 | $155,000 | 187 |
| Austin | $135,000 | $140,000 | 103 |
| Denver | $130,000 | $135,000 | 112 |
| National Median | $125,000 | $128,000 | 100 |
Source: Glassdoor, BEA Regional Price Parities
High-Value Skills That Boost Compensation
Specific skills command salary premiums above base expectations. For both roles, cloud expertise and modern tooling consistently add $10,000-$25,000.
Skills Premium Analysis
| Skill | Data Engineer Premium | Data Scientist Premium | Notes |
|---|---|---|---|
| Apache Spark | +$15,000 | +$10,000 | Critical for big data |
| Kubernetes/Docker | +$12,000 | +$8,000 | MLOps crossover |
| dbt/Modern Stack | +$10,000 | +$5,000 | Hot for analytics eng |
| Deep Learning/PyTorch | +$5,000 | +$20,000 | DS specialization |
| AWS/GCP Certs | +$15,000 | +$10,000 | Cloud is essential |
| Streaming (Kafka) | +$18,000 | +$8,000 | Real-time systems |
Source: LinkedIn Salary Insights, Glassdoor
Career Trajectory Comparison
Career paths diverge at senior levels. Data Engineers often move into Platform Engineering, Architecture, or Infrastructure leadership. Data Scientists branch into ML Engineering, Research, or Product leadership.
Typical Career Progressions
Data Engineer → Staff/Principal Engineer
Technical track focusing on system design, platform architecture, and scaling data infrastructure. Leads to Principal Engineer ($220K+) or Director of Data Platform roles. Strong path for those who love building systems.
Data Engineer → Data Architect
Design-focused role defining data strategy and governance across organizations. Less hands-on coding, more stakeholder work. Typical salary: $180K-$250K.
Data Scientist → ML Engineer
Production-focused path implementing models at scale. Combines DS skills with engineering rigor. Growing demand with $150K-$200K salary range.
Data Scientist → Research Scientist
R&D path at tech companies and research labs. Often requires PhD. Lower volume but high compensation ($180K-$300K at FAANG).
Both → Management
Engineering Manager, Data Science Manager, or Director roles. Requires people skills beyond technical expertise. Typical: $200K-$350K at senior levels.
Which Should You Choose?
The right choice depends on your interests, background, and career goals. Neither role is universally 'better'—they optimize for different outcomes.
Decision Framework: Which Role Fits You?
| Consider Data Engineering | Consider Data Science | |
|---|---|---|
| Enjoy building systems | ✓ Strong fit | May find limiting |
| Love statistics & math | Not essential | ✓ Core requirement |
| Want job stability | ✓ Higher stability | Project-dependent |
| Prefer stakeholder work | Less frequent | ✓ Daily interaction |
| Have SWE background | ✓ Natural transition | Requires reskilling |
| Have PhD/research | May be overqualified | ✓ Often preferred |
Source: Career analysis
This analysis combines federal employment data, crowdsourced compensation databases, and industry surveys to provide comprehensive salary comparison.
Coding Bootcamps: An Alternative Pathway
Coding bootcamps offer an accelerated pathway into tech careers. For those considering alternatives to traditional degrees, here's what you need to know about this intensive learning format.
What is a Coding Bootcamp?
A coding bootcamp is an intensive, short-term training program (typically 12-24 weeks) that teaches practical programming skills through hands-on projects. Unlike traditional degrees, bootcamps focus exclusively on job-ready skills and often include career services to help graduates land their first tech role.
Who Bootcamps Are Best For
- Career changers looking to enter tech quickly
- Professionals wanting to upskill or transition roles
- Self-taught developers seeking structured training
- Those unable to commit to a 4-year degree timeline
What People Love
Based on discussions from r/codingbootcamp, r/cscareerquestions, and r/learnprogramming
- Fast-track to employment—many graduates land jobs within 3-6 months
- Hands-on, project-based learning builds real portfolio pieces
- Career services and interview prep included in most programs
- Strong alumni networks for job referrals and mentorship
- Structured curriculum keeps you accountable and on track
Common Concerns
Honest feedback from bootcamp graduates and industry professionals
- Intense pace can be overwhelming—expect 60-80 hour weeks
- Some employers still prefer traditional CS degrees for certain roles
- Quality varies widely between programs—research carefully
- Job placement stats can be misleading—ask for CIRR audited reports
- May lack depth in computer science fundamentals like algorithms
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More Bootcamp Resources
Frequently Asked Questions
Continue Your Research
Data Sources and References
Occupational Employment and Wage Statistics (OES) May 2024
Crowdsourced tech compensation data
Salary reports and company reviews
2023-2033 job outlook data
Taylor Rupe
Co-founder & Editor (B.S. Computer Science, Oregon State • B.A. Psychology, University of Washington)
Taylor combines technical expertise in computer science with a deep understanding of human behavior and learning. His dual background drives Hakia's mission: leveraging technology to build authoritative educational resources that help people make better decisions about their academic and career paths.