MLOps Engineer vs Data Architect: Which Pays More?
Side-by-side salary comparison by city, experience level, and career growth outlook. Data reflects current market rates.
MLOps Engineer
Build and maintain infrastructure for deploying machine learning models at scale.
Data Architect
Design enterprise data systems, lakes, and pipelines for scalability and governance.
Data Architect earns more on average — the national median is $51,485/year (52%) higher than a MLOps Engineer. However, salaries vary significantly by city, employer, and experience level — see the city-by-city breakdown below.
MLOps Engineer vs Data Architect — Salary by City
National median figures in USD across top cities.
| City | MLOps Engineer | Data Architect | Difference |
|---|---|---|---|
| San Francisco, CA | $144,519 | $223,662 | MLOps $79,143 |
| New York, NY | $119,390 | $176,070 | MLOps $56,680 |
| Seattle, WA | $139,997 | $213,160 | MLOps $73,163 |
| Austin, TX | $107,475 | $169,668 | MLOps $62,193 |
| Chicago, IL | $108,552 | $163,518 | MLOps $54,966 |
| Boston, MA | $133,124 | $192,776 | MLOps $59,652 |
| London, UK | £79,867 | £114,371 | MLOps $34,504 |
| Toronto, Canada | CA$130,108 | CA$200,067 | MLOps $69,959 |
Frequently Asked Questions
Does a MLOps Engineer or Data Architect earn more?+
A Data Architect earns more on average. The national median salary for a MLOps Engineer is $99,130/year, compared to $150,615/year for a Data Architect — a difference of $51,485 (52%).
Which has better career growth — MLOps Engineer or Data Architect?+
MLOps Engineer roles are growing at 52% YoY while Data Architect demand is growing at 28% YoY. MLOps Engineer has stronger near-term demand growth.
Can you switch from MLOps Engineer to Data Architect?+
Yes. Many professionals transition between these roles, especially since both are in the same category. Shared skills include: analytical thinking, communication, and industry knowledge.
Which is harder to automate — MLOps Engineer or Data Architect?+
MLOps Engineer has a lower AI automation risk (22% vs 28%). Based on Oxford Martin School and McKinsey 2023 analysis.