GIS Analyst - LiDAR and Remote Sensing

Job Details

Job Location:

SGF - Springfield, MO 65804

Education Level: 4-Year Degree

Salary Range: $32.00 - $42.00 Hourly

Travel Percentage:
Up to 25%

Job Shift: Day

Job Category: GIS Specialist

• Process and analyze UAV and other LiDAR datasets from raw data through final deliverables.
• Perform point cloud classification, refinement, and quality control.
• Evaluate LiDAR accuracy using ground control, checkpoints, and established accuracy standards.
• Identify and troubleshoot alignment, positioning, classification, coordinate system, and other data quality issues.
• Generate geospatial products including classified LAS/LAZ, terrain and surface models, contours, break lines, planimetric mapping derived from LiDAR and imagery, orthomosaics, 3D models, profiles, cross-sections, and other project-specific deliverables.
• Extract features and measurements from LiDAR and imagery for engineering, surveying, utility, and infrastructure applications.
• Work with aerial imagery, photogrammetry, thermal imagery, and other remotely sensed datasets as project needs require.
• Apply appropriate coordinate systems, projections, datums, and transformations to geospatial datasets.
• Perform quality control and validation to ensure deliverables meet project requirements and applicable accuracy standards.
• Help develop efficient, repeatable workflows and technical standards as our LiDAR and remote sensing capabilities continue to grow.
• Evaluate new software, technologies, and processing methods that can improve quality, efficiency, or the services we provide.

• Produce maps, exhibits, spatial analyses, and geospatial datasets supporting engineering, surveying, utility, and infrastructure projects.
• Support GIS data conversion, migration, schema development, and quality control.
• Develop and maintain GIS datasets, web maps, feature services, and field data collection solutions.
• Assist with utility GIS and asset inventory projects involving electric, fiber, and other infrastructure.
• Automate repetitive GIS and data processing tasks using Python, Arc Py, Model Builder, or similar tools.
• Collaborate with GIS professionals, engineers, surveyors, field personnel, and project managers to solve geospatial problems and develop effective project solutions.

• Bachelor's degree in a geospatial-related field, or equivalent professional experience.
• Professional experience processing and analyzing LiDAR or point cloud data.
• Experience with point cloud processing software such as DJI Terra, LP360, Terra Solid, Global Mapper Pro, Cloud Compare, or comparable platforms.
• Working knowledge of ArcGIS Pro and geospatial data processing.
• Strong understanding of coordinate systems, projections, datums, and geospatial transformations.
• Understanding of GNSS, ground control, checkpoints, and positional accuracy concepts.
• Ability to evaluate geospatial data for accuracy and identify potential quality issues.
• Strong attention to detail and commitment to producing accurate, reliable deliverables.
• Ability to manage multiple datasets and project priorities while communicating technical issues clearly.

• UAV-based LiDAR acquisition and processing.
• DJI Zenmuse L1/L2 and DJI Terra.
• Other UAV LiDAR platforms such as ROCK Robotic, Yellow Scan, Phoenix LiDAR, or similar systems.
• Point cloud classification and automated feature extraction.
• Planimetric mapping and topographic data production.
• Photogrammetry and orthomosaic production.
• Electric utility, transmission, distribution, fiber, or other infrastructure projects.
• LiDAR data supporting PLS-CADD, PLS-POLE, CAD, or engineering design workflows.
• Utility GIS platforms such as NISC, Milsoft, Futura, or Vetro.
• Python, Arc Py, Model Builder, or other geospatial automation tools.
• ArcGIS Enterprise, ArcGIS Online, Field Maps, or Survey
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• Surveying, geomatics, or survey-supporting geospatial workflows.

The ideal candidate is technically strong, detail-oriented, and takes ownership of the quality of their work. They can work independently through complex geospatial datasets, identify inconsistencies or potential quality…