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Spatial Data Jobs in Colorado (NOW HIRING)

Data & Database Management: Proficiency in creating and maintaining enterprise geodatabases (ArcSDE), SQL, and spatial data integration. Scripting & Automation: Strong skills in Python (ArcPy/API ...

Front End React Developer

Denver, CO ยท On-site

$107K - $124K/yr

Material-UI * Experience working with Geographic Information Systems (GIS) and a basic understanding of geospatial concepts like spatial reference systems and spatial data representations * Source ...

Architect, Physical & Spatial AI, OT Transformation Join our AI & Engineering team in transforming ... The ideal candidate has worked across OT-heavy environments such as data centers, sports venues ...

Physical and Spatial AI Engineer III

Denver, CO ยท On-site

$59.25 - $79.50/hr

Physical & Spatial AI, Cloud Integrated Infrastructure Engineer III Join our AI & Engineering team ... This role will be supporting clients in environments such as data centers, sports venues and ...

The ideal candidate will have a strong background in spatial data analysis, GIS software, and map production, pipeline routing combined with a deep understanding of GIS concepts and best practices.

GIS Analyst

Denver, CO ยท On-site

$80K - $95K/yr

The ideal candidate will have a strong background in spatial data analysis, GIS software, and map production, pipeline routing combined with a deep understanding of GIS concepts and best practices.

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Spatial Data information

See Colorado salary details

$46.8K

$136.4K

$186.6K

How much do spatial data jobs pay per year?

As of Jun 9, 2026, the average yearly pay for spatial data in Colorado is $136,399.00, according to ZipRecruiter salary data. Most workers in this role earn between $120,400.00 and $144,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Spatial Data Analyst, and why are they important?

To excel as a Spatial Data Analyst, you need a strong background in geography, GIS, data analysis, and a relevant degree such as geography, environmental science, or computer science. Proficiency in GIS software (e.g., ArcGIS, QGIS), spatial databases (like PostGIS), and programming languages such as Python or R is typically required. Strong problem-solving abilities, attention to detail, and effective communication skills distinguish top performers in this field. These competencies are essential for accurately interpreting spatial data, generating actionable insights, and effectively sharing findings with stakeholders.

What are some typical challenges faced by spatial data analysts when working with large geospatial datasets?

Spatial data analysts often encounter challenges related to data quality and integration when working with large geospatial datasets. Issues such as inconsistent data formats, missing metadata, and varying spatial resolutions can complicate analysis. Additionally, managing the computational load of processing and visualizing large, complex datasets may require specialized software and robust hardware. Collaborating closely with GIS specialists, IT teams, and data engineers helps to address these challenges and ensure reliable results.

What is the difference between Spatial Data vs GIS Analyst?

AspectSpatial DataGIS Analyst
Required CredentialsGIS certifications, degrees in geography, GIS, or related fieldsGIS certifications, degrees in geography, GIS, or related fields
Work EnvironmentData collection, database management, mapping softwareData analysis, map creation, spatial problem-solving
Employer & Industry UsageUsed by GIS professionals, urban planners, environmental agenciesEmployed in government, consulting firms, environmental organizations
Search & Comparison IntentUnderstanding data types, data managementAnalyzing spatial data, creating maps, reports

Spatial Data refers to the raw geographic information used in mapping and analysis, while a GIS Analyst actively interprets, analyzes, and visualizes this data to support decision-making. Both roles require similar credentials and are integral to GIS projects, but Spatial Data is the foundational information, whereas GIS Analysts focus on applying that data to solve spatial problems.

What is spatial data?

Spatial data, also known as geospatial data, refers to information about the physical location and shape of objects on Earth. This data is usually stored as coordinates and topology and can represent features such as buildings, roads, rivers, or even entire countries. Spatial data is used in mapping, geographic information systems (GIS), urban planning, environmental studies, and various other fields to analyze locations, patterns, and relationships. It can be stored in formats like vector (points, lines, polygons) or raster (grids, images). Understanding spatial data is essential for making informed decisions based on geographic information.
Data Warehousing Specialist

Data Warehousing Specialist

Agelix Consulting

Lakewood, CO โ€ข On-site

Full-time

Posted 6 days ago


Job description

Job Summary for Senior Spatial Data Warehousing Specialist
- Lead the design, development, and implementation of data warehouses and data lakes, including geospatial and alphanumeric data.
- Integrate, consolidate, and geospatially enable data to support mapping, reporting, geospatial analysis, and self-service business intelligence (BI) needs.
- Utilize a variety of data warehousing tools and techniques, including traditional and modern data stack approaches, specifically for spatial data.
- Collaborate with project team members and leadership through effective task planning, project management, and coordination.
- Develop proof of concept and prototype reports, dashboards, and maps to help users visualize and analyze warehouse data.
- Support end users in mapping, reporting, and BI activities by publishing and maintaining accessible and reliable data sources.
- Apply advanced data analysis and data presentation skills to demonstrate the business value of spatial data environments.
- Bring at least 5 years of experience in building data warehouses (with spatial data) and developing data visualizations such as dashboards, reports, and maps.