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Geospatial Data Engineer Jobs (NOW HIRING)

Bridge Core (BCore)

Geospatial Programmer

Mclean, VA · On-site

Data Modeling & Databases - Experience modeling geospatial data and working with relational ... Data Engineering & Infrastructure - Scalable data pipeline development, performance optimization ...

aqua IT

Geospatial Engineer (Jr-Sr)

Saint Louis, MO · On-site

Collect, analyze, validate, and manage geospatial data using GIS, remote sensing, and GPS tools ... Bachelor's degree in geography, GIS, geospatial engineering, computer science, or related field ...

Urban SDK

Data Engineer

$117.20K - $140.70K/yr

About the role We are looking for a skilled Data Engineer to design, build, and maintain scalable ... Manage large-scale geospatial and temporal datasets stored in AWS S3. * Collaborate with data ...

VRK IT Vision Inc

GeoSpatial Developer

Mount Laurel, NJ · On-site

... data processing capabilities. · Collaborate with cross-functional teams to integrate geospatial ... developers in geospatial technologies and programming practices. · Stay updated with the latest ...

Second-61 LLC

Geospatial Solutions Architect

Doral, FL · Remote

Collaborate with data, analytics, and engineering teams to align geospatial capabilities with mission needs * Troubleshoot and optimize GIS applications and services * Duties and responsibilities may ...

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How much do geospatial data engineer jobs pay per hour?

As of May 30, 2026, the average hourly pay for geospatial data engineer in the United States is $46.63, according to ZipRecruiter salary data. Most workers in this role earn between $35.82 and $57.69 per hour, depending on experience, location, and employer.

What is a Geospatial Data Engineer job?

A Geospatial Data Engineer is responsible for designing, developing, and managing systems that process and analyze spatial data. They work with geographic information systems (GIS), databases, and cloud platforms to handle large-scale geospatial datasets. Their role involves data pipeline development, spatial analysis, and optimizing geospatial data storage and retrieval. They collaborate with analysts, scientists, and developers to support location-based decision-making.

What are the key skills and qualifications needed to thrive in the Geospatial Data Engineer position, and why are they important?

To thrive as a Geospatial Data Engineer, you need solid expertise in geospatial data processing, spatial databases, GIS concepts, and programming languages like Python or SQL, typically backed by a relevant degree in geoinformatics, computer science, or a related field. Familiarity with tools such as ArcGIS, QGIS, PostGIS, and cloud platforms, as well as certifications like GISP, are highly valued. Strong analytical thinking, attention to detail, and collaborative communication enhance performance in multidisciplinary teams. These skills are vital for accurately transforming complex spatial data into actionable insights and delivering reliable solutions in geospatial projects.

What are some common challenges faced by Geospatial Data Engineers on the job?

Geospatial Data Engineers frequently encounter challenges related to integrating large and diverse spatial datasets from multiple sources, ensuring data quality, and optimizing data for efficient querying and analysis. Managing changing project requirements and staying updated with evolving geospatial technologies are also common aspects of the role. In addition, collaborating with data scientists, analysts, and GIS specialists requires clear communication to translate technical data into actionable outputs. Navigating these challenges effectively helps engineers deliver robust geospatial solutions that support business and research goals.
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Geospatial Data Engineer jobs near you
Infographic showing various Geospatial Data Engineer job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 87% In-person, and 13% Remote job distribution, with an average salary of $96,989 per year, or $46.6 per hour.

Software/Data Engineer - Geospatial Algorithms with Security Clearance

Aqua IT

Herndon, VA

$117.60K - $141.30K/yr

Other

Posted 10 days ago

Job description

Software Engineer – Geospatial Algorithms Responsibilities:
• Demonstrated familiarity with software engineering practices specifically applied to geospatial data processing and the development of spatial algorithms at scale
• Strong understanding of vector geospatial data products and their associated processing pipelines, including ingestion, transformation, and quality validation workflows
• Thorough understanding of modern software engineering principles and cloud-native environments, with the ability to design and deploy scalable, production-grade geospatial solutions Qualifications/Skills Required:
• TS/SCI with CI Poly required • Must be willing to work in SCIF daily or as needed
• 5+ years of professional software engineering experience with cloud-first development approach
• 5+ years of hands-on experience with AWS CDK for Infrastructure as Code
• 3+ years building complex, reusable CDK constructs and stacks
• Proficiency in async programming and performance optimization* 3+ years implementing multi-environment deployment strategies using CDK
• 5+ years of advanced Python development experience
• 4+ years of data engineering experience
• ETL/ELT pipeline development using AWS services
• Experience with data lakes, data warehousing, and analytics platforms
o Real-time data processing and streaming architectures
• 3+ years with AWS cloud-native services
o Advanced knowledge of Lambda, API Gateway, EventBridge, SQS, SNS
o Experience with data services: RDS, DynamoDB, Redshift, S3, Athena
o Container orchestration with ECS, Fargate, or EKS
• Strong problem-solving abilities with proven track record of resolving complex technical challenges Preferred Qualifications:
• 3+ years of software engineering experience with a focus on geospatial data processing and spatial algorithm development
• Hands-on experience working with vector geospatial data products and familiarity with common processing workflows, including ingestion, transformation, and quality validation
• Strong command of modern software engineering principles and cloud-native environments, with demonstrated ability to build and deploy scalable, production-grade geospatial solutions

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