# Wind Energy Engineers

> Design underground or overhead wind farm collector systems and prepare and develop site specifications.

- **SOC code:** 17-2199.10
- **Canonical URL:** https://singulariki.com/roles/role-17-2199-10
- **Also known as:** Engineer, Project Engineer, Utility Engineer, Wind Energy Consultant, Turbine Measurements Engineer, Wind Farm Siting and Development Consultant, Wind Turbine Design Engineer, SCADA Engineer (Supervisory Control and Data Acquisition)
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Create or maintain wind farm layouts, schematics, or other visual documentation for wind farms.
- Provide engineering technical support to designers of prototype wind turbines.
- Recommend process or infrastructure changes to improve wind turbine performance, reduce operational costs, or comply with regulations.
- Investigate experimental wind turbines or wind turbine technologies for properties such as aerodynamics, production, noise, and load.
- Create models to optimize the layout of wind farm access roads, crane pads, crane paths, collection systems, substations, switchyards, or transmission lines.
- Develop active control algorithms, electronics, software, electromechanical, or electrohydraulic systems for wind turbines.
- Develop specifications for wind technology components, such as gearboxes, blades, generators, frequency converters, or pad transformers.
- Test wind turbine components, using mechanical or electronic testing equipment.
- Oversee the work activities of wind farm consultants or subcontractors.
- Test wind turbine equipment to determine effects of stress or fatigue.
- Monitor wind farm construction to ensure compliance with regulatory standards or environmental requirements.
- Direct balance of plant (BOP) construction, generator installation, testing, commissioning, or supervisory control and data acquisition (SCADA) to ensure compliance with specifications.

**Emerging tasks** (O*NET):
- Analyze meteorological data.
- Design electrical interconnections.
- Design wind turbine components.
- Estimate energy production by analyzing wind data.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Engineering and Technology _(knowledge)_
- Mathematics _(knowledge)_
- Design _(knowledge)_
- Physics _(knowledge)_
- English Language _(knowledge)_
- Computers and Electronics _(knowledge)_
- Critical Thinking _(essential_skill)_
- Deductive Reasoning _(ability)_
- Reading Comprehension _(essential_skill)_
- Written Comprehension _(ability)_
- Inductive Reasoning _(ability)_
- Mathematical Reasoning _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Physics _(Specialized Skill)_
- English Language _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Inductive Reasoning _(Common Skill)_
- Microsoft PowerPoint _(Common Skill)_
- Microsoft Excel _(Common Skill)_
- Linux _(Specialized Skill)_
- Writing _(Common Skill)_
- Systems Analysis _(Specialized Skill)_

**Tools & technology:**
- C++ _(hot technology, in demand)_
- Dassault Systemes SolidWorks _(hot technology, in demand)_
- Git _(hot technology, in demand)_
- Linux _(hot technology, in demand)_
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Python _(hot technology, in demand)_
- The MathWorks MATLAB _(hot technology, in demand)_
- Amazon Web Services AWS software _(hot technology)_
- Apache Subversion SVN _(hot technology)_
- Autodesk AutoCAD _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 69th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 67th percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 70th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 71st percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 9th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 2.1% growth (About average); 9.3k annual openings; 158.8k → 162.1k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $117,750; 150,750 employed.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-17-2199-10_