Customer Support - GIS Data Quality Specialist

Position Overview The GIS Data Quality Specialist is responsible for researching, analyzing, and resolving exceptions identified within LightBox’s location intelligence and geospatial data platforms. This role uses GIS tools, SQL, aerial imagery, parcel data, address datasets, building footprints, and other authoritative sources to validate and improve location-related records.   This position is focused on data quality, structured research, exception resolution, documentation, and process consistency. The GIS Data Quality Specialist applies established business rules and procedures to evaluate records, determine appropriate resolutions, and support the accuracy and reliability of LightBox’s geospatial and property data. The ideal candidate is highly analytical, detail-oriented, and comfortable working independently within structured and repeatable workflows. Success in this role requires strong GIS knowledge, SQL proficiency, sound research judgment, excellent documentation skills, and a commitment to accuracy and consistency when reviewing high volumes of data. What you will do and achieve Research and resolve exceptions identified through automated data quality, validation, and exception management processes. Analyze location-related records using GIS tools, aerial imagery, parcel data, address datasets, building footprints, geocodes, and other authoritative sources. Execute SQL queries to investigate data anomalies, compare records across datasets, validate data accuracy, and support exception resolution. Apply established business rules, procedures, and quality standards to determine appropriate resolutions for flagged records. Document research findings, decisions, and supporting information in accordance with internal quality, audit, and process documentation standards. Maintain productivity and quality standards while processing a structured queue of exception-based research tasks. Identify trends, recurring exception patterns, root causes, and data quality issues and provide feedback to improve processes, automation, and data integrity. Collaborate with data operations, GIS, product, and related teams to support data quality, process improvement, and operational efficiency. Assist in the development, refinement, and maintenance of research procedures, quality controls, standard operating procedures, and training documentation. Use GIS platforms and related tools to review spatial relationships, validate location accuracy, interpret imagery, and assess property-related records. Support continuous improvement efforts that increase the accuracy, consistency, efficiency, and scalability of geospatial data quality processes. This is a non-exempt position within the applicable salary range based on a 40-hour work week. Who you areEducation Bachelor’s degree in Geography, GIS, Geospatial Science, Information Systems, Computer Science, Data Analytics, or a related field required, or equivalent work experience. GIS certification or advanced coursework in geospatial analysis, database management, spatial data science, data analytics, or a related field preferred. Experience 3+ years of experience in GIS, geospatial analysis, location intelligence, land records research, data quality operations, or a related analytical field. Experience writing SQL queries for data investigation, validation, comparison, and exception resolution. Experience using ArcGIS Pro, ArcGIS Online, QGIS, PostGIS, or similar GIS platforms. Experience working with large datasets and performing structured research using repeatable processes, documented procedures, and quality standards. Experience evaluating addresses, parcels, building footprints, geocodes, property records, or other location-based data strongly preferred. Experience documenting research findings, applying business rules, and maintaining quality standards in a structured production or operations environment preferred. Key Knowledge & Skills Strong SQL proficiency, including the ability to query, compare, validate, and investigate records across datasets. Strong GIS analysis skills, including experience using GIS tools to review spatial data, validate records, and interpret location-based information. Knowledge of spatial datasets, parcels, addresses, building footprints, geocodes, aerial imagery, and property-related data. Ability to interpret aerial imagery, parcel boundaries, address data, geospatial relationships, and authoritative source information. Exceptional attention to detail and a strong commitment to data accuracy, consistency, and quality. Strong written documentation skills, including the ability to clearly record research findings, decisions, and supporting rationale. Ability to work independently, manage a structured workload, and maintain productivity and quality standards. Comfort performing repetitive analytical work while maintaining accuracy, focus, and consistency. Ability to learn, interpret, and consistently apply detailed business rules, procedures, and quality standards. Strong problem-solving skills with the ability to identify data discrepancies, evaluate possible causes, and determine appropriate resolutions. Core Competencies Analytical Thinking - Evaluates data, identifies patterns, investigates discrepancies, and applies sound judgment to resolve geospatial and location-related exceptions. Attention to Detail - Reviews records, source information, imagery, and data relationships carefully to ensure accuracy and consistency. Data Quality Focus - Demonstrates a strong commitment to improving the accuracy, reliability, completeness, and usability of LightBox’s geospatial data. Independent Work Management - Manages a structured queue of work, maintains productivity, and completes tasks accurately with limited day-to-day supervision. Process Adherence - Follows established business rules, procedures, documentation standards, and quality controls consistently. Consistency and Accuracy - Applies the same standards across high volumes of work while maintaining precision and reliable decision-making. Time Management - Prioritizes work effectively, meets deadlines, and maintains productivity in a structured operational environment. Continuous Improvement - Identifies recurring issues, shares feedback, and supports improvements to processes, automation, and data quality practices.