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Internship Google Earth Engine Jobs in Indiana (NOW HIRING)

Eightelevengroup

Sr. Data Scientist

Indianapolis, IN · On-site

$110.21 - $121.23/hr

Familiarity with cloud-native geospatial tools (PostGIS on AWS RDS, Google Earth Engine, etc.) (nice to have) * Exposure to spatial data infrastructure (GeoServer, MapServer, Mapbox, Deck.gl) (nice ...

Brooksource

Sr. Data Scientist

Indianapolis, IN · On-site

$80 - $88/hr

Familiarity with cloud-native geospatial tools (PostGIS on AWS RDS, Google Earth Engine, etc.) (nice to have) * Exposure to spatial data infrastructure (GeoServer, MapServer, Mapbox, Deck.gl) (nice ...

ERM

Visual Simulations Technician

Indianapolis, IN

$95K - $121K/yr

Experience with Google Earth Pro. * Experience with additional Adobe Creative Suite products ... Experience with Unreal Engine or Unity for real-time interactive environments. Key Responsibilities:

Erm

Visual Simulations Technician

Indianapolis, IN

$95K - $121K/yr

Experience with Google Earth Pro. * Experience with additional Adobe Creative Suite products ... Experience with Unreal Engine or Unity for real-time interactive environments. Key Responsibilities:

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Internship Google Earth Engine information

What is the difference between Internship Google Earth Engine vs Data Analyst Intern?

AspectInternship Google Earth EngineData Analyst Intern
Required SkillsBasic programming, GIS, remote sensing, Google Earth Engine platform knowledgeData analysis, Excel, SQL, visualization tools
Work EnvironmentEnvironmental, geospatial, remote sensing projectsBusiness, finance, marketing, or research sectors
Industry UsageGeospatial analysis, environmental monitoring, climate studiesData interpretation, reporting, decision support

Internship Google Earth Engine focuses on geospatial data analysis using Google Earth Engine, ideal for environmental and remote sensing projects. In contrast, Data Analyst Interns work with broader data sets across industries, emphasizing data interpretation and visualization. Both roles require analytical skills but differ in technical tools and industry focus.

What are the key skills and qualifications needed to thrive as an internship in Google Earth Engine?

To succeed in a Google Earth Engine internship, you need a solid background in remote sensing, geospatial analysis, and programming languages like Python or JavaScript, often supported by ongoing or completed studies in geography, environmental science, or computer science. Familiarity with the Google Earth Engine platform, GIS software (such as QGIS or ArcGIS), and data visualization tools is typically required. Strong analytical thinking, problem-solving abilities, and effective communication help you interpret results and collaborate within diverse project teams. These skills are crucial for efficiently processing geospatial data, deriving valuable insights, and contributing to impactful environmental or societal projects.

What types of projects do interns typically work on during a Google Earth Engine internship?

As a Google Earth Engine intern, you can expect to work on projects involving large-scale geospatial data analysis, development of tools or scripts for environmental monitoring, or collaborating with external partners on research initiatives. Interns often contribute to real-world solutions, such as mapping deforestation, monitoring urban growth, or developing visualizations for climate data. You'll typically work closely with experienced engineers, data scientists, and researchers, gaining exposure to both the technical and collaborative aspects of the platform. This hands-on experience helps build valuable skills in cloud computing, remote sensing, and data visualization.

What is an internship in Google Earth Engine?

An Internship in Google Earth Engine is a temporary position where students or recent graduates work with Google’s cloud-based geospatial processing platform. Interns typically assist with analyzing satellite imagery, developing geospatial applications, and supporting environmental or research projects. This role helps interns gain valuable experience in remote sensing, programming, and data analysis, while contributing to real-world solutions using Google Earth Engine. Interns often collaborate with experienced engineers, researchers, and other professionals, making it a great learning opportunity.

What are popular job titles related to Internship Google Earth Engine jobs in Indiana?

For Internship Google Earth Engine jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Internship Google Earth Engine jobs in Indiana look for?

The top searched job categories for Internship Google Earth Engine jobs in Indiana are:

What cities in Indiana are hiring for Internship Google Earth Engine jobs?

Cities in Indiana with the most Internship Google Earth Engine job openings:

Internship Google Earth Engine jobs near you
Infographic showing various Internship Google Earth Engine job openings in Indiana as of June 2026, with employment types broken down into 84% Full Time, 15% Part Time, and 1% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution.

Sr. Data Scientist

Eightelevengroup

Indianapolis, IN • On-site

$110.21 - $121.23/hr

Other

Posted 11 days ago

Job description

Geospatial Data Scientist

Remote

This is a Remote role.

Compensation: $80 - $88 per hour

ABOUT THE ROLE

Our client is seeking a Geospatial Data Scientist to transform complex spatial data into actionable insights and support product and business decision-making. In this role, you will work across the full geospatial data pipeline—from data ingestion and processing to analysis, modeling, and visualization—and collaborate closely with engineering and product teams to embed spatial intelligence into our platform. You will be responsible for developing and maintaining spatial data models, applying machine learning techniques to spatial problems, and creating compelling visualizations for diverse stakeholders. The ideal candidate thrives in a fully remote, asynchronous environment and brings a solid understanding of geospatial standards, coordinate reference systems, and data quality management.

WHAT YOU'LL DO
  • Design and execute geospatial analyses to support product and business decision-making
  • Build, validate, and maintain spatial data models and pipelines
  • Query and manage geospatial datasets using PostgreSQL with PostGIS
  • Work with geospatial data formats including GeoJSON, Shapefile, GeoTIFF, WKT, and WKB
  • Develop machine learning models with spatial components (clustering, classification, interpolation, etc.)
  • Create maps, dashboards, and visualizations to communicate findings to technical and non-technical stakeholders
  • Collaborate with backend engineers to integrate geospatial features into production systems
  • Evaluate and maintain geospatial data quality, coverage, and accuracy
  • Apply GIS tools (QGIS, ArcGIS, or equivalent) for spatial analysis and visualization
  • Ensure clear communication of geospatial insights in a remote, async environment
  • Maintain familiarity with geospatial standards, coordinate reference systems, and spatial indexing
  • Contribute to spatial data infrastructure and cloud-native geospatial workflows as needed
WHAT YOU BRING
  • 3–6 years of experience in data science, GIS, or a related field
  • Strong proficiency in Python for geospatial data analysis and modeling (GeoPandas, Shapely, Fiona, Rasterio, or similar)
  • Deep experience with PostgreSQL and PostGIS for spatial querying and data management
  • Familiarity with geospatial standards and formats (GeoJSON, Shapefile, GeoTIFF, WMS/WFS, WKT, WKB, etc.)
  • Experience with GIS tools such as QGIS, ArcGIS, or equivalent
  • Solid understanding of coordinate reference systems (CRS), projections, and spatial indexing
  • Experience applying machine learning techniques to spatial problems
  • Ability to communicate findings clearly in a fully remote, async environment
  • Experience with remote sensing or satellite imagery analysis (nice to have)
  • Familiarity with cloud-native geospatial tools (PostGIS on AWS RDS, Google Earth Engine, etc.) (nice to have)
  • Exposure to spatial data infrastructure (GeoServer, MapServer, Mapbox, Deck.gl) (nice to have)
  • Experience with big geospatial data processing (Apache Sedona, H3, S2) (nice to have)
  • Knowledge of Docker and containerized data workflows (nice to have)
  • Familiarity with CI/CD and version control best practices (nice to have)
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