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Spatial Data Science Jobs in Oregon (NOW HIRING)

$101K - $156K/yr

Collaborate with research scientists, software developers, and customer-facing teams to resolve challenges. * Stay informed about emerging tools and methods in spatial omics data analysis.

$101K - $156K/yr

Collaborate with research scientists, software developers, and customer-facing teams to resolve challenges. * Stay informed about emerging tools and methods in spatial omics data analysis.

OR · Hybrid

... spatial processors. * Publish and present technical work on novel compilation approaches for ... MS or PhD in Computer Science, Electrical/Computer Engineering, or related field, or equivalent ...

Deep knowledge of physical geography, human geography, map reading and spatial analysis, climate ... Guides students through reading topographic and thematic maps, analyzing population data and ...

Deep knowledge of physical geography, human geography, map reading and spatial analysis, climate ... Guides students through reading topographic and thematic maps, analyzing population data and ...

Monitor and improve deployed models based on real-world data and user feedback. * Mentor and ... Experience with geometry-heavy or spatial understanding problems, or multi-modal/sensor-fusion ...

Deep knowledge of physical geography, human geography, map reading and spatial analysis, climate ... Guides students through reading topographic and thematic maps, analyzing population data and ...

Impact You'll Own: * Guide your team of software engineers and data scientists to deliver ... Interest in defense, intelligence or spatial imagery. #LI-REMOTE Application Deadline: May 16, 2026 ...

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Showing results 1-20

Spatial Data Science information

See Oregon salary details

$47K

$137.1K

$187.7K

How much do spatial data science jobs pay per year?

As of Jun 10, 2026, the average yearly pay for spatial data science in Oregon is $137,147.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,100.00 and $145,400.00 per year, depending on experience, location, and employer.

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.

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.

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.

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 Oregon? For Spatial Data Science jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Spatial Data Science jobs in Oregon look for? The top searched job categories for Spatial Data Science jobs in Oregon are:
What cities in Oregon are hiring for Spatial Data Science jobs? Cities in Oregon with the most Spatial Data Science job openings:
Infographic showing various Spatial Data Science job openings in Oregon as of June 2026, with employment types broken down into 86% Full Time, 7% Part Time, and 7% Contract. Highlights an 60% In-person, 13% Hybrid, and 27% Remote job distribution, with an average salary of $137,147 per year, or $65.9 per hour.
Data Engineer / Database Management Specialist for Fish Data

Data Engineer / Database Management Specialist for Fish Data

Pacific States Marine Fisheries Commission

Portland, OR

Other

Posted 11 days ago


Job description

Pacific States Marine Fisheries Commission (PSMFC) is recruiting candidates to fill the role of amid-levelData Engineer/Database Specialist, working closely with USGS researchers at the Klamath Falls Field Station. The incumbent willsupport long-term research on endangered Lost River and Shortnose suckers, helping translate decades of biological, telemetry, and environmental data into insights that guide species recovery and water management in the Klamath Basin.

Thisrolemeaningful responsibilityand real-world impact. The incumbent willdesign and build modern, cloud-enabled data systems, improve legacy workflows, and collaborate directly with scientists to advance critical research.

The position is based in Portland, Oregon and includes regular travel to Klamath Falls (approximately one week per month)toworkon-sitewith theUSGSresearchers, with a potentialoptiontorelocateto Klamath Falls, Oregon.

Group Definition:Database Management Specialists develop and administer large-scale, multi-agency database systems. They have responsibility for maintenance, tuning, running backup and recovery, growing the system, administering permissions and security, and assuring continuous system availability.

Closing Disclaimer

PSMFC will not accept resumes or cover letters for any position once the job announcement has been removed from the Careers Page. PSMFC reserves the right to remove a job announcement at any time for any reason or close the position prior to the posted close date once a sufficient number of applicants have been received.

Our Team

The incumbent willjoin a small, highly collaborative team working closely with USGS scientists to turnresearchneeds intotechnical solutions. The incumbent will performquery and analyze data, support modeling and system performance, and help modernize workflows. In this role, the incumbent willalso collaborate with IT on security and access, contribute to data governance practices, and support GIS and field data collection systems, while helping teammates grow through knowledge sharingand collaboration.

Project Specifics

Contributeto the design andmodernizationdata systems that support fisheries research and real-world decision making in the Klamath Basin. Build reliable, scalable data platforms, improve legacy systems, and collaborate closely with scientists to turn complex data into actionable insights.Wedon'texpect candidates to bring experience in every listed technology-strength in core data skills and a willingness to learn are what matter most.

Essential Functions

The data professional should have 3+ years of experience and can design, build, and improve scientific data systems. The ideal candidate brings strong data engineering and database skills, along with the ability to collaborate effectively with scientists and IT partners.

Data & Platform Management

  • Design, build, andmaintainrelational databases (e.g., SQL Server, SQLite)
  • Perform data modeling, schema design, and performance optimization
  • Implement reliability practices (backup/recovery, access controls, availability)
  • Maintain documentation and support data stewardship practices
  • Troubleshoot and resolve database performance and integrity issues

Data Engineering & Integration

  • Build and support automated data pipelines (ETL/ELT)
  • Use SQL and scripting tools (Python, R, or similar) for data processing
  • Integrate diverse datasets into centralized systems
  • Refactor legacy scripts and workflows into scalable, automated pipelines

Analytics & Data Products

  • Develop queriesand reporting solutions (e.g., Power BI)
  • Build reproducible analytical workflows inPython or R

Collaboration

  • Partner with scientists, engineers, and IT staff
  • Communicate technical concepts to non-technical audiences

SupportModernization

  • Support migration of legacy systems into modern, cloud-enabled environments
  • Contribute to ongoing system and workflow improvements

Additional Mandatory Skills:

Databasesand Data Engineering

  • 3+ years of experience in data engineeringand/ordatabase administration

Programming languages:

  • Strong SQLproficiencyand at least one scripting language (Python or R)

Additional Desirable Skills:

Data &Data Architecture

  • Experience with data architecture and system design

CloudandModern Platforms

  • Knowledge ofcloud platforms (Azure preferred; AWS/GCP acceptable)
  • Familiarity with workflow automation or orchestration tools

Analytics & Visualization

  • Knowledgeofdashboards,PowerBIor similar data visualization tools
  • Familiarity AIassistedtools

GISTechnology andField Data Systems

  • ExposuretoGIS systems (ArcGIS, QGIS) or spatial datais a plus
  • Support ingestion workflows for field/mobile data collection systems (e.g., data from ESRI Survey123).

Data Governance & Lifecycle Management

  • Contributeto improvingdata governance or metadata practices
  • Familiarity with data lifecycle management and long-term data stewardship concepts

OperatingSystemsandLegacy Support

  • Support migrating legacy systems or applications(C#, VB)
  • Familiarity with Windows Server and/or Linux environments

Domain Experience

  • Familiarityworking with scientific, environmental, or ecological datasets
  • Familiarity with fisheries data,telemetry, tagging, or movement data

Collaboration and Leadership Skills

  • Interact withcontractors and withIT on security, permissions, and cloud migration (e.g., Azure)
  • Help team membersgrowby sharing knowledge andproviding guidance

KnowledgeRequired bythePosition:

  • Database management concepts, principles, and methods including database logical and physical design, normalization, storage capacity management, and back-up and recovery.
  • Data flow design and business process design.
  • Sources, characteristics, and uses of the organization's data assets.
  • Database management systems, query languages, table relationships, and views.
  • Data mining and data warehousing principles
  • The characteristics of physical and virtual data storage media.
  • Data administration and data standardization policies and standards in developing and managing large-scale, multi-agency databases.

PhysicalDemands:

Theworkissedentarywithmoderatewalkingbetweenworkstationsandcarryingfolders, reports, and similar light loads.

WorkEnvironment:

Theworkisperformedinanofficesettingwithadequatelighting,heatingandventilation.There are the normal risks of an office environment.