Backend Geospatial Engineer II

Lincoln Institute of Land Policy • Washington, DC

1d • $80,000 - $96,000 • Remote

About The Position

The Center for Geospatial Solutions (CGS) is seeking a Backend Geospatial Engineer II to build and maintain the data pipelines, APIs, and services for CGS’ geospatial programs. This role will primarily focus on a complex, multi-year federal program and a second program focused on water reservoir system analysis and data interoperability. The role involves evaluating and ensuring the quality, consistency, and defensibility of datasets, remote sensing products, analytical workflows, and AI-generated outputs. Reporting to the Lead Cloud Engineer, the position will split time between core backend engineering (designing data models, building and maintaining APIs and processing pipelines, and integrating geospatial and cloud services) and owning the QA/QC layer (designing validation logic, automating testing, benchmarking datasets, and integrating quality-assurance practices). The successful candidate will combine solid backend software engineering skills with geospatial domain knowledge and a strong quality mindset, working closely with data scientists, AI engineers, cloud engineers, GIS professionals, project managers, and subject-matter experts.

Requirements

• Bachelor's degree or equivalent experience in computer science, software engineering, information systems, engineering, geography, environmental science, or related technical field.
• 3+ years of professional backend software development experience, including meaningful work in geospatial data.
• Experience working with geospatial APIs and data interoperability standards such as pygeoapi.
• Knowledge of geospatial data architecture (e.g., PostGIS, GDAL, raster and vector data) and accompanying standards (e.g., OGC).
• Proficiency in Python for backend development, including building APIs and data pipelines.
• Working knowledge of geospatial data standards and architecture (e.g., OGC standards) and how they inform system design.
• Experience validating remote sensing, machine learning, deep learning, image classification, segmentation, or predictive modeling outputs.
• Experience with cloud platforms and production operations (deployment, monitoring, CI/CD) for cloud-based data and compute services, such as AWS, GCP or Azure.
• Familiarity with CI/CD practices.
• Experience with Git/GitHub or comparable version-control workflows.
• Experience designing and conducting accuracy assessments, validation studies, sampling strategies, or quantitative quality evaluations.
• Strong knowledge of spatial statistics, uncertainty, bias, error metrics, and appropriate interpretation of analytical results.
• Strong documentation skills and experience maintaining clear, traceable records of methods, findings, and decisions.
• Exceptional attention to detail and the ability to perform independent, objective technical review.
• Excellent written and verbal communication skills, including the ability to explain technical limitations and risk to varied audiences.
• U.S. Citizen, or legally authorized to work in the U.S. with no need for future sponsorship.

Nice To Haves

• Familiarity with proprietary geospatial software, such as Esri’s ArcGIS Pro and ArcGIS Enterprise.
• Experience with wetland science, field delineation, hydrology, geomorphology, ecology, or environmental regulatory applications.
• Experience designing or managing field data collection, expert review, or imagery interpretation protocols.
• Experience with cloud-based data pipelines, automated testing, data contracts, or production QA systems.
• Familiarity with federal scientific standards, data-quality objectives, reproducibility requirements, or formal verification and validation practices.
• Experience authoring peer-reviewed publications, validation reports, technical methods, or audit-ready documentation.
• Experience supporting government, nonprofit, consulting, or mission-driven technical projects.

Responsibilities

• Build and maintain production-quality backend services, APIs, data-access layers, workflow services, and reusable software components using Python or another appropriate language.
• Develop and optimize data models and storage solutions for raster and vector geospatial data (e.g., PostGIS, cloud-native geospatial formats).
• Build and maintain ETL/ELT pipelines that ingest, process, and transform geospatial and remote sensing data at scale.
• Integrate cloud services for data storage, compute, and orchestration of geospatial workflows.
• Write clean, maintainable, well-tested code and participate in code review and architecture decisions.
• Contribute to CI/CD pipelines for infrastructure, backend applications, data workflows and analytical services.
• Design and implement automated tests, as well as data validation and data quality checks, as part of the program development cycle.
• Build checks for data completeness, consistency, schema conformance, spatial integrity, metadata, and analytical accuracy directly into data pipelines and services.
• Conduct accuracy assessments, error analysis, uncertainty analysis, spatial cross-validation, bias checks, and other quantitative evaluations where needed.
• Create, curate and maintain benchmark and reference datasets for automated validation and regression testing.
• Document validation methods, test results and acceptance decisions.
• Identify data gaps, methodological weaknesses, and limitations that could materially affect interpretation or delivery.
• Collaborate with CGS colleagues to establish organization-wide QA and validation standards, templates, checklists, review gates, and documentation practices.
• Promote reproducible practices, including versioning of data, code, methods, assumptions, and outputs.
• Analyze recurring defects or process failures and recommend/implement improvements that reduce risk and rework.
• Stay current with relevant scientific, geospatial, statistical, and data-quality standards.
• Communicate validation findings, uncertainty, limitations, and recommendations clearly to technical and non-technical audiences.
• Contribute technical and QA documentation to reports, client deliverables, proposals, presentations and publications.
• Coordinate development and QA activities with project managers and technical leads to deliver on scope and schedule.
• Support client and partner discussions regarding system reliability, data quality and appropriate use.

Benefits

• 3x employer contribution towards retirement matching your employee contribution up to 15%
• health insurance
• dental insurance
• vision insurance
• 100% reimbursement of the health care deductible through a health reimbursement account
• short-term disability coverage
• long term disability coverage
• paid parental leave
• voluntary insurances such as accident insurance
• health care flexible spending
• dependent care flexible spending
• paid time off for holidays, vacation, personal, sick, bereavement, and jury duty
• office closure between December 24 – Jan 1 each calendar year
• flexible schedule and option for a compressed 4 day workweek
• tuition and staff development reimbursement
• pet insurance
• Employee Assistance Program

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level