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Source-backed profile

Operating Engineers and Construction Equipment Operators

Operate one or several types of power construction equipment, such as motor graders, bulldozers, scrapers, compressors, pumps, derricks, shovels, tractors, or front-end loaders to excavate, move, and grade earth, erect structures, or pour concrete or other hard surface pavement. May repair and maintain equipment in addition to other duties.

BLS SOC 47-2073 · O*NET-SOC 47-2073.00 · BLS 2025–2035 · O*NET 31.0

Median annual wage, 2025

$59,850

Projected change, 2025–2035

4.6%

2025–2035 annual average openings

39,200

AI exposure

Low

Editorial analysis

The machine is learning to assist the operator

Construction equipment is becoming more intelligent. Excavators, graders, loaders, and other machines can increasingly use positioning systems, cameras, sensors, automated grade control, and software that helps an operator work more precisely. AI adds another layer by interpreting the environment and helping optimize how equipment is used.

Automation may arrive one function at a time

A machine does not have to become completely autonomous to change the occupation. Automated digging limits, collision warnings, terrain mapping, machine guidance, and semi-automated repetitive movements can reduce the amount of manual control required for particular tasks.

  • Operators may increasingly supervise automated machine functions rather than control every motion directly.
  • Equipment data can help predict maintenance needs and reduce downtime.
  • Digital site models can guide excavation and grading with greater precision.
  • Changing soil, nearby workers, unexpected underground conditions, weather, and crowded jobsites still require situational judgment.

Operators who are comfortable with machine-control technology may have an advantage as equipment becomes more sophisticated. The career is likely to evolve toward a combination of traditional operating skill and the ability to manage increasingly automated machinery.

Labor-market picture

Two measures, different questions

These measures offer two different ways to understand the occupation: BLS projections show how national employment may change over time, while relative AI exposure highlights where tasks may intersect with AI-related capabilities. Read them with the work profile and your local context to decide what deserves closer investigation.

Employment

Employment, 2025
489,500 jobs
Projected employment, 2035
511,800 jobs
Projected change, 20252035
22,300 jobs (4.6%)

Relative AI exposure

BLS category: Low. This is a relative task-exposure classification, not a forecast of employment change, automation, wages, or worker replacement.

Relative AI exposure · Low

Work profile

Representative tasks

These task statements come from O*NET 31.0. They are source facts, not labels assigned by the AI exposure measure.

  • Learn and follow safety regulations.
  • Take actions to avoid potential hazards or obstructions, such as utility lines, other equipment, other workers, or falling objects.
  • Start engines, move throttles, switches, or levers, or depress pedals to operate machines, such as bulldozers, trench excavators, road graders, or backhoes.
  • Coordinate machine actions with other activities, positioning or moving loads in response to hand or audio signals from crew members.
  • Align machines, cutterheads, or depth gauge makers with reference stakes and guidelines or ground or position equipment, following hand signals of other workers.
  • Locate underground services, such as pipes or wires, prior to beginning work.

Skills that matter

Skills in the source profile

Importance uses O*NET's 1–5 scale. Level uses its 0–7 scale. Select a skill to see its source definition and reported values.

Technology and tools

These technology records come from O*NET and show software or tools reported in the occupation profile. They describe work context, not a required checklist for every employer.

  • Facilities management software
  • Spreadsheet software
  • Office suite software
  • Electronic mail software
  • Operating system software
  • Time accounting software

Preparation

Typical entry education
High school diploma or equivalent
Related experience
None
On-the-job training
Moderate-term on-the-job training

Ask JobShift

Ask Dr. Morgan Reed about this evidence

Morgan is the JobShift Atlas analyst powered by AI. Answers draw from the evidence on this page and explain what it can tell you.

Source provenance

Labor-market and exposure facts: bls-ep-2025-35. Work profile: onet-31.0. Verify release details on the Sources page.