Urbanize, Inc.

Compensation

Full Description

Company Overview Urbanize Inc. is a technology company focused on accelerating equitable and sustainable housing development through advanced geospatial intelligence. Our mission is to empower developers, investors, and policymakers with precise, data-driven insights that make affordable housing development faster, more transparent, and more efficient. We combine expertise in GIS, data engineering, and urban planning to deliver an integrated platform that aggregates parcel-level, zoning, environmental, and market data, enabling users to identify high-opportunity sites for affordable housing with confidence. By developing cutting-edge data infrastructure, Urbanize transforms complex geospatial datasets into actionable intelligence that supports Low-Income Housing Tax Credit (LIHTC) project analysis, site scoring, and investment strategy. Our team is committed to rigorous data quality, innovation, and the belief that better spatial information leads to more equitable and sustainable communities. Position Summary We seek a GIS Data Engineer to serve as a cornerstone of our technical team, responsible for the comprehensive management of geospatial parcel data that powers our LIHTC development platform. This position demands expertise in extracting, cleaning, harmonizing, and generating geospatial data at scale—focusing on parcel-level information that supports affordable housing development decisions. The successful candidate will architect and maintain robust data processing pipelines that integrate diverse spatial datasets, implement sophisticated spatial analysis methodologies, and deliver reliable, high-quality data products. The ideal candidate thrives in a fast-paced environment, demonstrates exceptional problem-solving skills, and maintains an unwavering commitment to data accuracy, performance, and scalability. Core Responsibilities Data Architecture and Pipeline Development • Design and implement scalable ETL workflows capable of processing parcel data from public, purchased, and web-scraped sources. • Develop automated pipelines integrating APIs such as OpenStreetMap, Google Places, and Mapbox with data harmonization workflows. • Build and maintain robust PostGIS database architectures optimized for spatial query performance and data integrity. • Establish version control systems for geospatial data and implement backup and recovery procedures ensuring business continuity. Spatial Analysis and Methodology Development • Develop advanced spatial methodologies for LIHTC project evaluation based on Qualified Allocation Plan (QAP) scoring criteria. • Implement proximity, isochrone, and buffer-based analysis methods for assessing access to amenities, employment centers, and services. • Create algorithms that convert spatial relationships into quantitative metrics used for project ranking and investment prioritization. • Integrate zoning, census, and environmental datasets into unified analytical frameworks supporting affordable housing policy analysis. Data Quality and Control Systems • Implement robust quality assurance and validation systems to detect spatial inconsistencies, coordinate errors, and data anomalies. • Develop visualization tools to verify spatial calculations and ensure transparency in methodology for stakeholders and clients. • Create repeatable error correction workflows enabling rapid issue resolution and continuous improvement of data accuracy. • Establish cross-validation systems to verify coordinate systems, projections, and calculated metrics across datasets. Performance Optimization and Scalability • Continuously optimize data pipelines to improve efficiency and scalability using distributed frameworks such as Apache Spark. • Develop caching mechanisms, batch processing strategies, and asynchronous workflows to maximize processing speed and reliability. • Monitor and resolve performance bottlenecks, balancing system cost and throughput across cloud and on-premise infrastructure. • Apply parallel processing techniques and performance tuning strategies for geospatial datasets exceeding millions of records. Technical Requirements The GIS Data Engineer must possess strong technical expertise across data management, programming, and geospatial analysis domains. This includes mastery of PostGIS for spatial database administration, advanced Python scripting for automation, and experience with modern ETL frameworks. • Spatial Databases, Expert: PostGIS, PostgreSQL • Programming Languages, Expert: Python, SQL • GIS Software, Advanced: ArcGIS, QGIS • Data Processing, Advanced: ETL Workflows, batch processing • Web APIs, Intermediate: Google Places, Mapbox API, OpenStreetMap • Version control, Intermediate: Git, Github Preferred Technical Skills • Experience with distributed computing frameworks such as Apache Spark. • Familiarity with cloud platforms (AWS, GCP, Azure) for scalable data infrastructure. • Knowledge of Docker or containerized environments for reproducible deployments. • Experience with web scraping tools and techniques for automated data collection. • Familiarity with data visualization libraries or frameworks for spatial QA and analytics. Domain Knowledge Requirements LIHTC Program Understanding • Comprehensive understanding of the Low-Income Housing Tax Credit (LIHTC) program, including Qualified Allocation Plans (QAPs). • Familiarity with state housing finance agency processes and affordable housing regulatory frameworks. • Knowledge of site selection, zoning compliance, and community impact considerations in project development. • Understanding of affordable housing policy, financing mechanisms, and stakeholder relationships. Geospatial Analysis Expertise • Deep understanding of spatial operations including proximity analysis, buffering, overlay analysis, and spatial joins. • Knowledge of coordinate systems, map projections, and reference systems for ensuring accuracy in multi-source integration. • Experience translating geospatial data into business intelligence, dashboards, and analytical reports. • Understanding of demographic, economic, and zoning data relevant to urban development and planning. Qualifications and Experience Educational Background A Bachelor's degree in Computer Science, Geography, Engineering, Urban Planning, or a related field is required. A Master's degree in Geographic Information Systems, Geospatial Technology, or a related discipline is highly desirable. Certifications in GIS, database administration, or cloud platforms (AWS, GCP, Azure) are advantageous. Professional Experience At least three years of experience in GIS data engineering, spatial database management, or similar technical roles. Experience working with large-scale spatial datasets, especially in real estate, environmental analysis, or public sector data, is highly valuable. Proven success in building production-level ETL pipelines, API integrations, and geospatial automation tools is essential. Performance Expectations and Success Metrics • Maintain data quality standards that ensure accuracy, completeness, and reliability across all spatial products. • Implement processing optimizations that increase throughput and reduce latency without compromising quality. • Contribute innovative methodologies that enhance spatial analysis, visualization, and decision support capabilities. • Demonstrate proactive problem-solving, leadership in data-driven initiatives, and commitment to continuous improvement. Working Environment and Expectations • Collaborate closely with developers, analysts, and business stakeholders to deliver data-driven insights. • Communicate complex spatial and technical concepts clearly to non-technical audiences. • Adapt effectively to shifting priorities, emerging technologies, and changing project requirements. • Operate efficiently under tight deadlines while maintaining high standards for data accuracy and performance. Compensation and Benefits • Competitive salary and equity package based on experience and qualifications. • Comprehensive professional development support including training, certifications, and conferences. • Flexible work arrangements promoting work-life balance and team collaboration. • A mission-driven, innovative, and collaborative company culture focused on impact and excellence.