Compare · Updated 16 September 2026
Data Analyst vs Machine Learning Engineer: which pays more and which is faster?
Same sourced data as the career pages, side by side.
Machine Learning Engineer pays more at mid-career: a median of $200,000 against $88,000 for Data Analyst, about 127% higher. Data Analyst is faster to enter: the quickest verified route takes about 6 months versus 18 for Machine Learning Engineer. Job growth favours Data Analyst (12% projected over ten years, BLS 2025-35, versus 10%).
| Data Analyst | Machine Learning Engineer | |
|---|---|---|
| Entry median | $68,000 | $140,000 |
| Mid-career median | $88,000 | $200,000 |
| Senior median | $115,000 | $270,000 |
| Top end | $154,571 | $497,500 |
| Roadmap hours | 630 | 1,360 |
| Fastest way in | Analytics bootcamp (6 mo) | Data engineer to ML engineer (18 mo) |
| Cheapest way in | $0 | $800 |
| Time to first job | 9–18 months | 36–60 months |
| Degree | No degree is legally required to work as a Data Analyst and a portfolio genuinely substitutes for one at small and mid-size employers, but most large-company postings still list a bachelor's degree as a preference. | No degree is legally required, but Machine Learning Engineer is the least degree-flexible of the data roles in practice: most large-employer postings list a bachelor’s degree in computer science or another quantitative field, and a master’s is still the standard credential for new entrants. A portfolio substitutes for it only when it contains deployed systems or open-source contributions, not notebooks. |
| 10-year growth | 12% | 10% |
| Openings per year | 7,500 | 106,100 |
| Automation exposure | medium | low |
| Key certification | Google Data Analytics Professional Certificate | AWS Certified Machine Learning Engineer - Associate (MLA-C01, succeeded by MLA-C02) |
| Tools | SQL (PostgreSQL, Snowflake, BigQuery, SQL Server), Excel / Google Sheets, Power BI, Tableau, Looker / Looker Studio | Python, PyTorch, scikit-learn, XGBoost / LightGBM, Spark |
Salary figures checked September 2026 (Data Analyst) and September 2026 (Machine Learning Engineer). Sources are listed on each career page.
What a Data Analyst does
A Data Analyst is a person who turns a company's raw database into answers a manager can act on, using SQL, spreadsheets and a dashboard tool such as Power BI or Tableau.
A data analyst turns a messy company database into answers. The core loop is: a stakeholder asks something vague ("why did signups drop in July?"), you translate it into a query, pull the data with SQL, clean and check it, build a chart or a Power BI/Tableau dashboard, and write two paragraphs that say what happened and what to do. Most of the job is SQL and communication; the modelling is usually descriptive statistics, cohorts, funnels and the occasional A/B test readout. Titles vary a lot: business analyst, BI analyst, marketing analyst, product analyst, revenue operations analyst and healthcare data analyst are all the same skill set pointed at a different domain.
- Lowest barrier to entry of any six-figure-track data role: no degree requirement, no calculus, and a portfolio genuinely substitutes for credentials.
- Skills transfer across every industry, so you can follow the domain you find interesting or the one that pays.
- Mostly predictable hours - 40-45 a week outside month-end close and board-deck season.
What a Machine Learning Engineer does
A Machine Learning Engineer is a software engineer who owns machine learning models in production: the pipeline that assembles training data, the evaluation that decides whether a new model ships, the serving path that answers in milliseconds under load, and the monitoring that catches drift.
A machine learning engineer owns models in production. That means the pipeline that assembles training data, the training job itself, the evaluation harness that decides whether a new model is better than the old one, the serving path that answers in 50 milliseconds under load, and the monitoring that catches drift before a stakeholder does. At large companies you will specialise - ranking and recommendations, ads, search relevance, fraud and risk, speech or vision, or the platform team that builds the training infrastructure everyone else uses. At small companies you are the entire ML function, which means you also do the data engineering.
- Among the highest-paid individual contributor roles outside of finance: Levels.fyi US median total compensation of $280,000 and 90th percentile of $497,500.
- Work is intellectually dense and the feedback is objective - the model is better or it is not.
- Skills transfer cleanly to AI engineering, data engineering, backend engineering and research infrastructure.
How to choose between Data Analyst and Machine Learning Engineer
- Pick Data Analyst if internal transfer from a non-analyst job at your current employer produces more career-changer hires than cold applications do, because it converts domain knowledge you already have into the thing employers are actually short of; the certificate-plus-portfolio route works too, but it is the slowest of the five, at about 14 months and 150 to 300 applications.
- Pick Machine Learning Engineer if almost every Machine Learning Engineer hire in the United States comes from someone already employed as a software engineer, data engineer or data scientist who moved across after two to four years, usually inside the same company, because the interview loop tests production software engineering at the same bar as a backend engineer and that skill is built by doing the job rather than by coursework.
The natural next moves are analytics engineer, which pays more for deeper SQL, dbt and pipeline ownership, and Data Scientist, where the US Bureau of Labor Statistics median is $120,230 against $88,940 for the operations research analyst code that BLS uses for analyst work, and where a quantitative master's degree is a common filter. The natural moves are AI Engineer, which is a faster door in because the bar sits closer to a strong application developer’s, and Data Engineer or Software Engineer, which pay less at the top but hire far more readily. Moving toward research or Applied Scientist titles raises the degree bar sharply, because those postings usually want a PhD; moving toward platform and infrastructure work does not, and pays comparably.
Data Analyst vs Machine Learning Engineer FAQ
Which pays more, Data Analyst or Machine Learning Engineer?
At mid-career the median is $88,000 for a Data Analyst and $200,000 for a Machine Learning Engineer; at senior level $115,000 versus $270,000. Entry medians are $68,000 and $140,000. Figures are US base plus typical bonus where reported, checked September 2026.
Is it faster to become a Data Analyst or a Machine Learning Engineer?
The quickest verified route into Data Analyst is Analytics bootcamp at about 6 months; for Machine Learning Engineer it is Data engineer to ML engineer at about 18 months. Our full roadmaps run 630 and 1,360 study hours respectively.
Which is harder to automate, Data Analyst or Machine Learning Engineer?
We rate automation exposure medium for Data Analyst and low for Machine Learning Engineer. Large language models now write competent SQL and first-draft charts, which compresses the ticket-taking half of Data Analyst work and is one reason Indeed's Hiring Lab counted entry-level postings down 7.5 percent year over year in May 2026. Owning metric definitions, experiments and the stakeholder relationship is what does not automate. Code assistants now write much of the boilerplate a Machine Learning Engineer used to type, along with first-draft pipelines, tests and documentation, which is why the same loops now include an AI-assisted coding round. What does not automate is scoping the problem, designing the evaluation that decides whether a model ships, and owning a system whose failures are statistical rather than a stack trace.
Do I need a certification for Data Analyst or Machine Learning Engineer?
No certification is required to work as a Data Analyst. The one with genuine screening value is Microsoft's PL-300 (Power BI Data Analyst Associate) at $165 for the exam in the United States and 40 to 90 hours of study, and it is worth having if you are targeting Microsoft-shop employers in corporate, healthcare, insurance or government. The Google Data Analytics Professional Certificate costs about $147 to $294 over three to six months and is worth taking as a curriculum, but it carries little weight as a credential on its own: there is no proctored exam and 3.8 million people have enrolled. No certification is required for a Machine Learning Engineer role and no employer screens on one. The credential with real signal is the Certified Kubernetes Administrator at $445, because it is purely performance-based on live clusters, cannot be passed from question dumps, and Kubernetes is where serious training and inference workloads run - note that it expires after two years and renewal means sitting the whole exam again at full price. The AWS Certified Machine Learning Engineer - Associate at $150 and the Coursera specializations from Andrew Ng and DeepLearning.AI are curricula worth the study time, not credentials anyone hires on.