GIS Specialist – Utility Mapping, Data Quality & Data Acceptance Testing
Based in the United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3–5 years of professional GIS experience
- Required Skills
- Microsoft AccessSAPMicrosoft Excel
Requirements
- Bachelor’s degree in GIS, Geography, Geospatial Science, or a related field.
- 3–5 years of professional GIS experience.
- Experience supporting mapping, asset data maintenance, or utility data workflows.
- Experience working in structured QA/QC, data validation, or data acceptance testing environments preferred.
- Proficiency with enterprise GIS platforms, particularly Esri ArcGIS tools, and spatial data editing.
- Experience working with utility infrastructure data, including asset systems and engineering records.
- Strong understanding of geographic data types, including land base, imagery, and utility infrastructure datasets.
- Ability to follow defined workflows, validation standards, and acceptance criteria.
- Strong analytical, problem-solving, and troubleshooting skills.
- High attention to detail and commitment to data accuracy and quality.
- Proficiency with Microsoft Office Suite, including Word, Excel, and Access.
Responsibilities
- Perform GIS editing, data remediation, and maintenance activities for electric utility assets.
- Update and maintain spatial and attribute data according to established standards, workflows, and quality requirements.
- Execute Data Acceptance Testing (DAT) workflows across integrated and conflated GIS and asset datasets.
- Review and validate data outputs from multiple sources, including GIS, SAP, engineering records, and field-collected information.
- Assess spatial accuracy, attribute completeness, and data consistency resulting from data integration and reconciliation processes.
- Apply QA/QC procedures and acceptance criteria to determine whether datasets meet defined standards.
- Identify, document, and categorize defects, inconsistencies, and data quality issues.
- Support root cause analysis related to source system discrepancies, transformation processes, and data workflows.
- Collaborate with program managers, engineering teams, quality teams, and data stakeholders to resolve discrepancies.
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