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Spatial Data Jobs (NOW HIRING)

WaterSuite

Spatial Data Manager

Braintree, MA · On-site

Spatial Data Manager performs a key role in the operations of WaterSuite by owning and maintaining the spatial data model, overseeing and executing spatial data processes as well as working with ...

WaterSuite

Spatial Data Manager

Braintree, MA

Spatial Data Manager performs a key role in the operations of WaterSuite by owning and maintaining the spatial data model, overseeing and executing spatial data processes as well as working with ...

GRVTY

Data Steward -- Senior

Springfield, VA

$70.25 - $94/hr

You will apply your expertise in GIS, data schemas, and georeferencing to organize and maintain spatial data, ensuring alignment with established metadata and extraction standards. Leveraging ...

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Spatial Data information

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$44.5K

$129.7K

$177.5K

How much do spatial data jobs pay per year?

As of Jun 28, 2026, the average yearly pay for spatial data in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 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.
More about Spatial Data jobs
What cities are hiring for Spatial Data jobs? Cities with the most Spatial Data job openings:
What are the most commonly searched types of Spatial Data jobs? The most popular types of Spatial Data jobs are:
What states have the most Spatial Data jobs? States with the most job openings for Spatial Data jobs include:
What job categories do people searching Spatial Data jobs look for? The top searched job categories for Spatial Data jobs are:
Spatial Data jobs near you
Infographic showing various Spatial Data job openings in the United States as of June 2026, with employment types broken down into 13% As Needed, 10% Full Time, 3% Part Time, 7% Temporary, 60% Contract, and 7% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.
Spatial Data Manager

Spatial Data Manager

WaterSuite

Braintree, MA • On-site

Full-time

Posted 28 days ago

Job description

Company Description
WaterSuite is a cloud based geospatial platform housing a large and growing inventory of spatial data layers of all types and sizes from numerous sources. WaterSuite is a unique and valuable resource for the drinking water community, supporting source water protection and online monitoring efforts, and is in use by many clients, including drinking water utilities, municipalities and states.
Job Description
Spatial Data Manager performs a key role in the operations of WaterSuite by owning and maintaining the spatial data model, overseeing and executing spatial data processes as well as working with other team members to evolve the data model and processes to address emerging needs.
Responsibilities:
  • Owns the spatial data model (conceptual level down to attribute level) and ensures compliance
  • Owns the spatial data processes and ensures timely and orderly execution
  • Spatial data processes include periodic layer updates with change tracking for hundreds of layers
  • Keeps documentation on spatial data model and processes current
  • Curates newly requested spatial data layers
  • Works with team members to extend or modify the spatial data model
  • Works with team members to build new processes or improve existing ones
  • Advises application development team on spatial functionality
  • Responds to ad-hoc spatial data or analysis requests
  • Trains other team members on spatial data model and processes as needed
  • Participates in agile teams, collects/reviews user stories, extracts requirements, creates/curates tickets
  • Prepare/participate/present in company events, workshops, conferences or other venues

Qualifications
Required Skills/Experience:
  • Keen attention to detail
  • Experience working with spatial data at scale
  • Experience working with large databases
  • Experience with PostgreSQL, PostGIS, SQL
  • Understanding of GIS and spatial concepts, tools, processes
  • Understanding of basic relational database concepts
  • Able to handle multiple tasks in a fast-paced environment
  • Excellent verbal, written, and interpersonal communication skills
  • Collaborative problem solver
  • Critical thinking to evaluate alternative solutions

Preferred Skills:
  • Familiarity with drinking water concepts, hydrology, watershed science or similar
  • Degree in computer science, geography, environmental science or relevant field
  • GISP, PMP or other relevant certification
  • Programming skills in python, R, JavaScript or similar

Additional Information
Location: Remote initially then Braintree, MA (at exit 6 off Route 93)
All your information will be kept confidential according to EEO guidelines.
Keywords: PostgreSQL, Postgres, PostGIS, PG Admin, SQL, QGIS, ESRI ArcGIS, geospatial analysis and visualization, geography, cartography, hydrology, map making, emergency response, hazard/risk analysis, watershed, JIRA, Atlassian Confluence, Slack, OpenLayers, python, jupyter notebooks, Azure Data Studio, R, JavaScript, TypeScript, ETL, ogr2ogr, GDAL, Cassandra, Docker, Vue.js, REST API, Tableau, Google Sheets, Excel, Amazon AWS

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