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Spatial Data Science Jobs in Texas (NOW HIRING)

Arva Intelligence

Data Engineer

Houston, TX · On-site +1

$95K - $130K/yr

Experience working with geospatial data formats and spatial data processing * Experience supporting scientific or ecosystem modeling workflows preferred * Familiarity with workflow orchestration ...

Arva Intelligence

Data Engineer

Houston, TX · On-site +1

$95K - $130K/yr

Experience working with geospatial data formats and spatial data processing * Experience supporting scientific or ecosystem modeling workflows preferred * Familiarity with workflow orchestration ...

Arva Intelligence

Data Engineer

Houston, TX · On-site

$95K - $130K/yr

Experience working with geospatial data formats and spatial data processing * Experience supporting scientific or ecosystem modeling workflows preferred * Familiarity with workflow orchestration ...

CoStar Group

Land.com - Senior Data Scientist

Austin, TX · On-site

... spatial joins Preferred Qualifications: * Advanced degree in Economics, Data Science, Agricultural Economics, Statistics, Geospatial Science, Applied Mathematics, or Computer Science * Experience ...

CoStar

Land.com - Senior Data Scientist

Austin, TX · On-site

... spatial joins Preferred Qualifications: Advanced degree in Economics, Data Science, Agricultural Economics, Statistics, Geospatial Science, Applied Mathematics, or Computer Science Experience with ...

CoStar Group

Land.com - Senior Data Scientist

Austin, TX · On-site

... spatial joins Preferred Qualifications: * Advanced degree in Economics, Data Science, Agricultural Economics, Statistics, Geospatial Science, Applied Mathematics, or Computer Science * Experience ...

Staffingine LLC

Drone Data Engineer

Houston, TX · On-site

$109K - $131K/yr

Work closely with data science teams to operationalize AI models on drone data * Systems Cloud ... Knowledge of GIS tools spatial data formats and mapping systems * Cloud Systems * Experience with ...

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Cushman & Wakefield

Junior Data Scientist

Austin, TX

... data science role, preferably in a real estate, urban policy, planning, or economic research context. * Strong command of quantitative methods: regression,time series analysis,spatial econometrics ...

Cushman & Wakefield

Junior Data Scientist

Austin, TX

... data science role, preferably in a real estate, urban policy, planning, or economic research context. * Strong command of quantitative methods: regression,time series analysis,spatial econometrics ...

Cushman & Wakefield

Junior Data Scientist

Austin, TX

... data science role, preferably in a real estate, urban policy, planning, or economic research context. * Strong command of quantitative methods: regression,time series analysis,spatial econometrics ...

Cushman & Wakefield

Junior Data Scientist

Austin, TX

... data science role, preferably in a real estate, urban policy, planning, or economic research context. * Strong command of quantitative methods: regression,time series analysis,spatial econometrics ...

Permian Resources

GIS Platform Analyst

Midland, TX

... spatial data pipelines, and ensuring the reliability and integrity of GIS data across the ... BA/BS in Geography, Computer Science, or related field. * 4+ years of progressive GIS employment ...

Permian Resources

GIS Platform Analyst

Midland, TX · On-site

... spatial data pipelines, and ensuring the reliability and integrity of GIS data across the ... BA/BS in Geography, Computer Science, or related field. * 4+ years of progressive GIS employment ...

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All JobsSpatial Data Science JobsSpatial Data Science Jobs in Texas

Spatial Data Science information

See Texas salary details

$41.5K

$120.9K

$165.4K

How much do spatial data science jobs pay per year?

As of Jul 1, 2026, the average yearly pay for spatial data science in Texas is $120,851.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,700.00 and $128,100.00 per year, depending on experience, location, and employer.

What is the highest paying GIS job?

The highest paying GIS jobs are often senior roles such as GIS Director, Geospatial Data Scientist, or GIS Manager, with salaries exceeding $100,000 annually. These positions typically require advanced skills in spatial analysis, programming, and leadership, and may involve working with tools like ArcGIS, Python, or SQL.

What is spatial data science?

Spatial data science is a field that combines data science techniques with geographic information systems (GIS) to analyze and interpret spatial or location-based data. It involves collecting, processing, and visualizing data that has a geographic or spatial component, such as maps, satellite images, or GPS coordinates. Spatial data scientists use methods from statistics, machine learning, and computer science to solve problems related to urban planning, environmental monitoring, transportation, and more. The insights gained from spatial data science help organizations make better decisions based on the relationships and patterns found in geographic data.

Is GIS hard to get a job in?

Getting a job in GIS or spatial data science can be competitive, but having strong skills in GIS software like ArcGIS or QGIS, programming languages such as Python or R, and a solid understanding of spatial analysis can improve employability. Relevant certifications and a portfolio of projects also enhance job prospects in this field.

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

To thrive as a Spatial Data Scientist, you need a strong background in statistics, geospatial analysis, and programming (often with Python or R), typically supported by a degree in geography, computer science, or a related field. Proficiency with GIS software (such as ArcGIS or QGIS), spatial databases (like PostGIS), and relevant certifications (e.g., Esri Technical Certification) is commonly required. Strong analytical thinking, problem-solving abilities, and effective communication are vital soft skills to interpret spatial data and convey insights to stakeholders. These competencies are crucial for extracting actionable insights from complex geospatial datasets and supporting informed decision-making.

Is 40 too late for data science?

Age is generally not a barrier to entering a data science or spatial data science career, as skills and experience are more important. Many professionals successfully transition into data science later in life by acquiring relevant skills such as programming, statistics, and data visualization, often through online courses or certifications. Employers value diverse experiences, and continuous learning can help you stay competitive regardless of age.

What is the difference between Spatial Data Science vs Geospatial Analyst?

AspectSpatial Data ScienceGeospatial Analyst
Required CredentialsDegree in GIS, Geography, Data Science, or related fields; often includes certifications in GIS or data analysisDegree in Geography, GIS, or related fields; certifications in GIS software are common
Work EnvironmentData analysis, modeling, and programming; often in tech or research settingsMapping, data visualization, and GIS software use; typically in government, environmental, or urban planning agencies
Employer & Industry UsageTech companies, research institutions, urban planning, environmental agenciesGovernment agencies, environmental consultancies, urban planning firms

Spatial Data Science focuses on analyzing spatial data using advanced data science techniques, programming, and modeling. In contrast, Geospatial Analysts primarily work with GIS software to create maps and visualize spatial data. While both roles require GIS knowledge, Spatial Data Scientists often have stronger programming and statistical skills, working on complex data analysis projects, whereas Geospatial Analysts focus more on mapping and data visualization tasks.

Is GIS a high demand job?

GIS (Geographic Information Systems) professionals, including those in spatial data science, are in high demand across industries such as urban planning, environmental management, and transportation. The growing use of spatial analysis, remote sensing, and GIS software like ArcGIS and QGIS contributes to strong job prospects and competitive salaries in this field.

What are some typical challenges spatial data scientists face when integrating geospatial data from multiple sources?

Spatial data scientists often encounter challenges like inconsistencies in data formats, varying coordinate reference systems, and differences in spatial resolution when integrating geospatial data from multiple sources. Addressing these requires familiarity with data transformation tools and a strong understanding of spatial data standards. Additionally, ensuring data quality and managing large datasets can be complex, so attention to detail and effective use of GIS software are crucial for successful integration.
What are popular job titles related to Spatial Data Science jobs in Texas? For Spatial Data Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Spatial Data Science jobs in Texas look for? The top searched job categories for Spatial Data Science jobs in Texas are:
Spatial Data Science jobs near you

Data Engineer

Arva Intelligence

Houston, TX • On-site, Remote

$95K - $130K/yr

Full-time

Posted 13 days ago

Key responsibilities

  • Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling.

  • Develop and maintain cloud-based infrastructure and data storage solutions to support modeling pipelines and optimization workflows.

  • Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements.

Job description

Job Title:                          Data Engineer 

Department:                     Modeling & Analytics

Reports to:                       Lead Modeling Scientist

Location:                          Remote

Base Salary Range:        $95k - $130k

General Position Description

The Data Engineer is responsible for building and scaling the data and computational backbone that supports Arva’s ecosystem modeling and measurement, reporting, and verification platforms. This role sits within a multidisciplinary Data Science team and focuses on designing reliable, auditable, and scalable data systems that enable biogeochemical modeling and optimization at production scale.

In this role, the Data Engineer will design and maintain production-grade data pipelines that integrate diverse datasets including field measurements, management practices, soils, and weather with process-based ecosystem models. The role plays a critical part in ensuring data quality, reproducibility, and traceability so that scientific outputs can be translated into trusted, credit-grade results with real-world impact.

Primary Job Responsibilities

Data Pipeline and Workflow Development

  • Design, implement, and maintain scalable data pipelines supporting ecosystem and biogeochemical modeling
  • Build reproducible workflows that generate standardized model inputs and manage outputs across space, time, and scenario analysis
  • Integrate heterogeneous datasets, including field data, management data, soil data, and weather data, into modeling pipelines

Cloud Infrastructure and Data Systems

  • Develop and maintain cloud-based infrastructure to support modeling pipelines and optimization workflows
  • Implement data storage solutions using relational, spatial, and object-based databases
  • Support efficient data access and processing using platforms such as PostgreSQL, PostGIS, and cloud object storage

Data Quality, Governance, and Auditability

  • Ensure data quality, versioning, traceability, and auditability to support measurement, reporting, and verification requirements
  • Implement validation and monitoring processes to ensure reliability of model inputs and outputs
  • Support transparent, repeatable workflows suitable for regulatory and credit market review

Software Engineering and Collaboration

  • Write clean, modular, and well-documented production code that supports maintainable and scalable data systems
  • Apply software engineering best practices including testing, version control, and documentation
  • Collaborate closely with Data Science and Technology teams to align data infrastructure with modeling, analytics, and production needs

Key Competencies / Requirements

  • 3+ years demonstrated experience building and maintaining data pipelines for large, complex, and heterogeneous datasets
  • Strong proficiency in Python and modern data engineering tools, with experience writing production-grade, testable code
  • Experience working with cloud platforms, with AWS strongly preferred
  • Familiarity with containerization tools such as Docker and version control systems such as GitHub
  • Experience with relational and spatial databases, including PostgreSQL and PostGIS
  • Experience working with geospatial data formats and spatial data processing
  • Experience supporting scientific or ecosystem modeling workflows preferred
  • Familiarity with workflow orchestration tools such as Airflow or Prefect preferred
  • Bachelor’s or Master’s degree or equivalent experience in Data Engineering, Computer Science, Environmental Informatics, or a related field

Apply