Data Engineer vs ML Engineer
Data Engineer and ML Engineer earn comparably at the mid level — $110 000 – $150 000 and $120 000 – $160 000 respectively in the US (Source: Habr Career, Glassdoor 2025).
At a glance
| Data Engineer | ML Engineer | |
|---|---|---|
| Salary comparison | $110 000 – $150 000 | $120 000 – $160 000 |
| Training Duration | 6–18 months | 9–24 months |
| Job Search Duration | 3–9 months | 4–10 months |
| English Level | B1–B2 — for reading cloud docs and working with international data teams | B2 — for reading research papers and technical documentation |
| Education | Bachelor's in CS or STEM is common — a strong portfolio compensates for a missing degree | Technical degree with strong math background preferred — the math foundation is hard to build alone |
| Demand Trend | High Demand | High Demand |
Salary comparison
Data Engineer
United StatesSource: Habr Career, Glassdoor 2025
ML Engineer
United StatesSource: Habr Career, Glassdoor 2025
Skills compared
Data Engineer
Technical Skills
Soft Skills
ML Engineer
Technical Skills
Soft Skills
Key differences
- Data engineers build the pipelines and warehouse that deliver reliable data. ML engineers train and deploy models and own the training pipeline and model serving. One builds the data platform; the other builds the model lifecycle.
- Both write Python and both live in the data space, and ML depends on data engineering for clean inputs. The data engineer optimizes pipelines, quality, and scale; the ML engineer optimizes model performance and deployment. The two roles converge in MLOps.
Which path should you choose?
At the mid level, Data Engineer and ML Engineer pay comparably — $110 000 – $150 000 and $120 000 – $160 000 respectively in the United States, according to Habr Career, Glassdoor 2025. So the choice between them usually comes down to entry threshold and timeline rather than money: Data Engineer typically takes 6–18 months to learn and roughly 3–9 more to land a first role, while ML Engineer takes 9–24 and 4–10 months respectively.
If getting to market and earning sooner matters most, take the path with the shorter ramp. If you're willing to invest longer for a higher long-term ceiling, lean toward the role with the wider band. The skills and key-differences sections below show how close your existing background is to each option — and that fit, more than the salary number, is usually what makes the decision hold up.
If you're still early in the switch, the faster path has a real edge: it lets you validate the career change, start earning, and build a portfolio sooner, and that compounds — every month of delay is a month of senior-level pay you postpone. If you already have transferable experience, the higher-ceiling path rewards the deeper investment. The at-a-glance table above lays out the exact trade-off in months and pay, so match it against your own timeline and savings runway.
Frequently asked questions
Go deeper
Data Engineer
Build the pipelines that turn raw data into reliable analytics. Data engineers design warehouses, automate ETL/ELT flows, and make data trustworthy for analysts and scientists.
ML Engineer
Machine learning engineers build the AI systems that power recommendations, search, autonomous vehicles, and language models. It is one of the highest-paid and fastest-growing roles in technology.
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