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Spatial Data Jobs (NOW HIRING)

PepsiCo

Digital Product Sr Manager - Spatial Twin

Plano, TX · On-site

$140.10 - $234.85/hr

Define standards for spatial data models, 3D assets, metadata, and GIS integration. * Support integration with Teamcenter, Omniverse, Mendix, GIS platforms, and enterprise systems. User Experience ...

3B Staffing LLC

Sr. Python Developer

Irving, TX · On-site

$70/hr

We are seeking a skilled Python Developer with expertise in PostgreSQL database management, spatial data handling, and multiprocessing solutions. * Proven experience (8 years) in Python development ...

3B Staffing LLC

Sr. Python Developer

Irving, TX · On-site

$70/hr

We are seeking a skilled Python Developer with expertise in PostgreSQL database management, spatial data handling, and multiprocessing solutions. * Proven experience (8 years) in Python development ...

Expert Technology Services

Data Annotator

Irving, TX · On-site

$109K - $132K/yr

Data Annotator & QA Reviewer - Autonomy & Robotics (Mining) - Perform manual data annotation and ... spatial and temporal mapping. - Decompose mining workflows into structured task sequences, labeling ...

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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 Sep 4, 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 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.

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 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 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.

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 August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Senior Geospatial Data Scientist/Geospatial AI/ML Scientist

VDart, Inc.

Juno Beach, FL • On-site

Other

Posted 18 days ago

Job description

The Senior Data Scientist Geospatial is a technical leader responsible for designing, developing, and delivering advanced data science, spatial analytics, and machine learning solutions using geospatial data. This role leads complex analytics initiatives, develops scalable analytical applications and spatial data products, establishes technical standards, and mentors junior data scientists and technical team members.

The position partners closely with GIS teams, business stakeholders, and technology leaders to translate complex geospatial and business challenges into scalable, production-ready solutions that deliver measurable business impact.

Key Responsibilities
  • Lead end-to-end data science and geospatial analytics initiatives, from problem definition and data exploration through model development, deployment, and monitoring.
  • Design and develop predictive, prescriptive, and spatial analytics models using large-scale geospatial and enterprise datasets.
  • Architect and deliver scalable GIS tools, analytical applications, spatial data products, and reusable geoprocessing capabilities.
  • Establish technical standards, development frameworks, coding practices, and architectural patterns for enterprise geospatial solutions.
  • Design and oversee spatial data pipelines, APIs, automation workflows, and geoprocessing frameworks.
  • Develop production-grade analytical tools and applications using Python and modern data science technologies.
  • Integrate GIS capabilities with cloud platforms, enterprise data systems, APIs, and AI/ML services.
  • Engineer and optimize features, analytical workflows, and machine learning models for performance, scalability, and reliability.
  • Implement model validation, testing, monitoring, and governance frameworks.
  • Develop automated spatial analysis workflows and reusable geoprocessing components.
  • Collaborate with GIS professionals, data engineers, software engineers, and business leaders to deliver enterprise-level solutions.
  • Translate complex analytical and geospatial findings into clear, actionable recommendations for technical and non-technical stakeholders.
  • Provide technical leadership and mentorship to junior and mid-level data scientists.
  • Evaluate emerging GIS, AI, ML, and data science technologies and recommend solutions aligned with business objectives.
  • Apply strong business acumen and independent problem-solving skills to address complex analytical challenges.
Required Qualifications
  • Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Geography, GIS, Engineering, or another quantitative discipline; advanced degree preferred.
  • 6 10 years of professional experience in data science, analytics, machine learning, or a closely related field.
  • 6 8+ years of relevant experience for senior-level geospatial data science responsibilities.
  • Strong recent experience applying data science techniques to GIS/geospatial problems, preferably within the last 4 years.
  • Strong proficiency in Python or R, with demonstrated experience developing production-ready analytical workflows.
  • Advanced SQL skills and experience working with large-scale datasets.
  • Strong knowledge of statistical inference, machine learning, experimentation design, and predictive modeling.
  • Hands-on experience developing and deploying production-grade models, analytical applications, or data products.
  • Extensive experience with spatial analytics and geospatial data at scale.
  • Strong experience developing enterprise-grade GIS tools, applications, pipelines, and automation workflows.
  • Demonstrated ability to independently lead complex technical initiatives from concept through production.
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About V Dart

Sourced by ZipRecruiter

Industry

Software development, internet and it and professional, scientific, and technical services

Company size

1,001 - 5,000 Employees

Headquarters location

Alpharetta, GA, US

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