Remote GIS QA Engineer — ArcGIS & Data Validation

Mano Lani LLC.

United States

Remote

USD 95,000 - 120,000

Full time

2 days ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Benefits offered by this job

Vacation plan

Retirement plan with company match

Competitive salary

Job summary

Mano Lani LLC is seeking a Senior GIS Software QA Engineer to support a U.S. Army program. The role is remote and focuses on testing GIS applications, validating complex datasets, and ensuring automated tools produce accurate results.

Ideal candidates will have hands-on experience with ArcGIS Pro/Enterprise, SQL-based data validation, and automated testing. Collaboration with GIS specialists and developers is essential to deliver reliable solutions.

Qualifications

Bachelor of Science in a relevant field is required.
Experience developing and executing software test plans to identify software problems and their causes.
Experience testing GIS applications, geospatial data workflows, and automated tools.
Understanding of Esri ArcGIS systems and GIS data validation.
Ability to obtain a DoD CAC and work in a federal program environment.
Hands-on testing of GIS-enabled apps and data validation workflows.
Experience with SQL for data validation and comparing source data to outputs.
Ability to design and execute automated tests and data checks.

Responsibilities

Develop, document, and execute test plans for GIS applications and automated workflows.
Perform functional, regression, integration, and data validation testing to identify defects.
Test ArcGIS Pro tools, geoprocessing workflows, and automated reporting solutions.
Validate spatial data, geodatabase attributes, and relationships between GIS and Real Property records.
Test automated business rules to identify and correct dataset inconsistencies.
Verify RPUID corrections, missing attributes, and exception reporting.
Test GIS–Real Property alignment tools, dashboards, and web mapping apps.
Use SQL or scripts to compare source data with outputs and confirm accuracy.
Document defects with reproduction steps and evidence; collaborate to verify fixes.
Support automated testing processes to improve reliability and reduce manual testing.