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Profession JRI–40 / Technology

Will AI replace data analysts?

No role in our index has a stronger growth outlook: employment of data analysts is projected to rise 33.5% by 2034, from a base of 262,440, at a median of $120,230. That sits alongside real task exposure, since AI now writes queries, builds charts, and drafts the narrative around dashboards and reports.

The risk spectrum — where Data Analyst sits
Data Analyst vs the 83 other jobs
Risk rank40 / 84
Automation timeline2-5 years
Median salary$120,230
Projected growth+33.5% (2024–34)
01 / Your plan

Learn before your role changes.

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What you'll learn
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Updated Sep 21, 2026

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02 / Job market

Official outlook: still growing.

Within the 84 professions
Salary$120,230
$31k$171k
Growth+33.5% (2024–34)
-25.9%33.5%

The U.S. Bureau of Labor Statistics projects a +33.5% change in employment for this occupation between 2024 and 2034, from a current base of 262,440 people employed earning a median of $120,230 a year.

Source: BLS Employment Projections 2024–34 & OEWS May 2025.

AI adoption in postings →

03 / Real salaries

Data Analyst salaries by city

Median pay from real records (H-1B filings and tech job postings), across the cities with most observations.

New York$141,170Seattle$142,501San Francisco$169,000Bellevue$142,501Redmond$166,104San Jose$175,000

Range shows p25–p75; the dot is the median. Sources carry a documented bias — see the full salary page.

See real salaries →

04 / How to enter

How to become a data analyst.

Typical preparation

Job Zone Four: Considerable Preparation Needed

Education

Most of these occupations require a four-year bachelor's degree, but some do not.

Experience

A considerable amount of work-related skill, knowledge, or experience is needed for these occupations.

Job training

Employees in these occupations usually need several years of work-related experience, on-the-job training, and/or vocational training.

Getting in is one thing — the day-to-day runs on a specific set of tools and skills:

Foundations
Core skills
Tools

Full skill path with courses →

07 / FAQ

What people ask.

01Will AI replace data analysts?
With a projected increase of 33.5% by 2034, Data Analysts remain in demand even as AI handles query writing and chart building in 53% of observed use. The occupation pays $120,230 at the median, and the risk score sits at 55, so the work shifts toward interpretation rather than disappearing.
02How soon or how fast could that happen for Data Analysts?
Automation of routine reporting tasks is expected within 2-5 years. In 53% of observed cases, AI already augments the analyst by generating queries, charts, and first drafts, so the pace of change is tangible but not immediate.
03What skills keep Data Analysts relevant?
Demand is high for statistical modeling and causal inference, and for data storytelling; machine-learning fundamentals are medium. Those skills let analysts focus on causal reasoning and decisions leadership trusts, while AI handles mechanical query writing and charting.
04How does someone become one of Data Analysts, or what preparation is needed?
Preparation typically requires a four-year bachelor's degree, the standard credential for the field. Entry-level skills include critical thinking, reading comprehension, and active listening. Foundational knowledge in statistical modeling and machine-learning fundamentals helps, but demand is stronger for causal inference and storytelling.
05What related roles could Data Analysts pivot to?
Moving into marketing manager or software engineer roles is common for Data Analysts, with risk scores of 40 and 30 respectively. These positions use the analyst's data fluency while emphasizing strategy or engineering.
06Is Data Analyst a good career to start in 2026?
Starting as a Data Analyst in 2026 gives a 33.5% projected growth by 2034, though automation is expected within 2-5 years. The median pay is $120,230, and building skills in causal inference and data storytelling will matter.
07What tasks does AI now handle for Data Analysts?
AI writes queries, builds charts, and drafts the initial narrative around dashboards and reports. Observed use is roughly 53% augmenting the analyst, meaning the model does the mechanical work while the analyst focuses on sharper questions and causal reasoning.