Backend Geospatial Engineer II

LINCOLN INSTITUTE OF LAND POLICY•Washington, DC

16d•$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 data pipelines, APIs, and services for its geospatial programs. The position’s primary focus is a complex, multi-year federal program, alongside a program focused on water reservoir system analysis and data interoperability. Across these and future projects, the role emphasizes 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 combines backend engineering—designing data models, building and maintaining APIs and processing pipelines, and integrating geospatial and cloud services—with ownership of quality assurance and quality control. This includes designing validation logic, automating testing, benchmarking datasets, and embedding QA practices into projects. The engineer works with data scientists, AI engineers, cloud engineers, GIS professionals, project managers, and subject-matter experts to develop reliable systems and ensure products are fit for their intended use. CGS is a self-sustaining nonprofit enterprise and a fully remote team with geospatial expertise. It works with public, private, and nonprofit partners on challenges including housing affordability, ecosystem conservation, water management, and sustainable infrastructure. The Center is part of the Lincoln Institute of Land Policy, a nonprofit private operating foundation that seeks to improve quality of life through the effective use, taxation, and stewardship of land.

Requirements

Bachelor's degree or equivalent experience in computer science, software engineering, information systems, engineering, geography, environmental science, or a related technical field.
At least 3 years of professional backend software development experience, including meaningful work with geospatial data.
Experience working with geospatial APIs and data interoperability standards such as pygeoapi.
Knowledge of geospatial data architecture, including PostGIS, GDAL, and raster and vector data, and accompanying standards such as OGC.
Proficiency in Python for backend development, including building APIs and data pipelines.
Working knowledge of geospatial data standards and architecture, such as 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, including deployment, monitoring, and 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 ability to perform independent, objective technical review.
Excellent written and verbal communication skills, including the ability to explain technical limitations and risks to varied audiences.
Must be a U.S. citizen or legally authorized to work in the U.S. without need for future sponsorship.

Nice To Haves

Familiarity with proprietary geospatial software, such as Esri 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.
Develop and optimize data models and storage solutions for raster and vector geospatial data, including PostGIS and cloud-native geospatial formats.
Build and maintain ETL/ELT pipelines to 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 reviews and architecture decisions.
Contribute to CI/CD pipelines for infrastructure, backend applications, data workflows, and analytical services.
Design and implement automated tests, data validation, and data quality checks as part of program development.
Build pipeline and service checks for data completeness, consistency, schema conformance, spatial integrity, metadata, and analytical accuracy.
Conduct accuracy assessments, error and uncertainty analyses, spatial cross-validation, bias checks, and other quantitative evaluations as 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 or 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 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 about system reliability, data quality, and appropriate use.

Benefits

3x employer retirement contribution matching employee contributions up to 15%.
Health insurance; the Institute pays 90–95% of medical plan costs.
Dental insurance; the Institute pays all dental plan costs.
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 insurance options, including accident insurance.
Health care flexible spending account.
Dependent care flexible spending account.
Paid time off for holidays, vacation, personal, sick, bereavement, and jury duty.
Office closure between December 24 and January 1 each calendar year.
Flexible schedule and option for a compressed four-day workweek.
Tuition and staff development reimbursement.
Pet insurance.
Employee Assistance Program.