Customer Support, GIS Data Quality Specialist

🕒 July 28

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

💝 Customer Support

👻 Ghost score 19%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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LightBox

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2019

🏠 Real Estate

☁️ SaaS

🤝 B2B

Real Estate • SaaS • B2B

LightBox is a provider of connected property data, location intelligence, and workflow software for commercial real estate professionals. The company aggregates authoritative parcel, ownership, zoning, transaction, building footprint, geocoding, environmental and valuation datasets and exposes them through products such as LightBox Vision (formerly LandVision), APIs, bulk data delivery, valuation and marketing tools, and professional services. LightBox positions itself as a B2B SaaS platform that helps customers locate, analyze, and act on commercial real estate information for use cases including capital markets, environmental due diligence, lending, valuation, and government/utility workflows.

📋 Description

• 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.

🎯 Requirements

• 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. • 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.