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Spatial Data Manager Jobs in Indianapolis, IN (NOW HIRING)

Eightelevengroup

Sr. Data Scientist

Indianapolis, IN · On-site

$110.21 - $121.23/hr

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 ...

Bruker

Automation Chemist

Indianapolis, IN · On-site

$125K/yr

... spatial and single-cell biology, structural and condensate biology, as well as in clinical ... English fluency with strong oral, reading, written, MS Office, computer, technical data, and ...

All JobsSpatial Data JobsSpatial Data Manager JobsSpatial Data Manager Jobs in Indianapolis, IN

Spatial Data Manager information

See Indianapolis, IN salary details

$29.6K

$92.9K

$164.4K

How much do spatial data manager jobs pay per year?

As of Aug 26, 2026, the average yearly pay for spatial data manager in Indianapolis, IN is $92,857.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,100.00 and $120,000.00 per year, depending on experience, location, and employer.

What is a spatial data manager?

Spatial Data Managers are professionals responsible for organizing, maintaining, and overseeing spatial or geographic data within an organization. They ensure the accuracy, accessibility, and security of geospatial datasets used in mapping, analysis, and decision-making processes. Their role often involves using Geographic Information Systems (GIS), coordinating with other technical staff, and developing data management standards and protocols. Spatial Data Managers support various industries, including urban planning, environmental science, utilities, and transportation.

What are the key skills and qualifications needed to thrive as a spatial data manager?

To excel as a Spatial Data Manager, you need expertise in geospatial data analysis, spatial database management, and a degree in geography, GIS, or a related field. Familiarity with GIS software (such as ArcGIS or QGIS), database systems (like PostgreSQL/PostGIS), and data standards is typically required. Strong organizational skills, attention to detail, and effective communication are vital soft skills for managing complex datasets and collaborating with stakeholders. These abilities ensure accurate data management, efficient workflows, and informed decision-making in organizations reliant on spatial information.

What are some common challenges spatial data managers face when maintaining large geospatial databases?

Spatial Data Managers often encounter challenges related to data quality, integration of multiple data sources, and ensuring data consistency across platforms. Managing large volumes of geospatial data requires robust organizational systems and regular data validation to prevent errors or duplication. Additionally, they must stay up-to-date with evolving GIS technologies and standards while coordinating with cross-functional teams like analysts, engineers, and project managers to support various mapping and spatial analysis needs.

What is the difference between Spatial Data Manager vs GIS Analyst?

AspectSpatial Data ManagerGIS Analyst
CredentialsBachelor's or higher in GIS, Geography, or related field; GIS certificationsBachelor's or higher in GIS, Geography, or related field; GIS certifications
Work EnvironmentData management teams, GIS departments, urban planning firmsMapping projects, spatial analysis teams, environmental agencies
Industry UsageOrganizations managing large spatial datasets, government agenciesAnalysis and visualization of spatial data for projects
Search & ComparisonFocuses on data management, database systems, and infrastructureFocuses on spatial analysis, mapping, and GIS software applications

The main difference is that a Spatial Data Manager oversees the organization, storage, and maintenance of spatial datasets, ensuring data quality and accessibility. In contrast, a GIS Analyst primarily conducts spatial analysis and creates maps using GIS software. Both roles require similar credentials but serve different functions within GIS projects.

What are popular job titles related to Spatial Data Manager jobs in Indianapolis, IN?

For Spatial Data Manager jobs in Indianapolis, IN, the most frequently searched job titles are:

What job categories do people searching Spatial Data Manager jobs in Indianapolis, IN look for?

The top searched job categories for Spatial Data Manager jobs in Indianapolis, IN are:

Spatial Data Manager jobs near you
Infographic showing various Spatial Data Manager job openings in Indianapolis, IN as of August 2026, with employment types broken down into 86% Full Time, 11% Part Time, and 3% Contract. Highlights an 79% Physical, 2% Hybrid, and 19% Remote job distribution, with an average salary of $92,857 per year, or $44.6 per hour.

Sr. Data Scientist

Indianapolis, IN • On-site

Eightelevengroup
See more jobs at Eightelevengroupexternal

$110.21 - $121.23/hr

Other

Posted 20 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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