GIS Engineer

Location: Fully Remote within the US, Preferably East CoastUSC Only

Required Qualifications:

• Experience:
• 12+ years of overall professional experience.
• 8+ years working with open-source and COTS GIS software and databases (e.g., QGIS, ArcGIS, PostGIS, SDE) and their associated APIs or interfaces.
• Technical Expertise:
• Knowledge of best practices in data management, standards, quality, and interoperability.
• Proven ability to manage, convert, and maintain large volumes of vector geodata (e.g., shapefiles, geospatial databases, GeoJSON, GeoPackage, etc.).
• Hands-on experience maintaining, querying, and performing spatial analysis in PostGIS spatial databases and/or SQL Server Spatial/Oracle Spatial.
• Skilled in modeling, manipulating, processing, optimizing, and extracting value from large, complex, disconnected GIS datasets.
• Proficiency in one or more scripting languages (e.g., Python, R) with demonstrated experience in data-centric environments.
• Geospatial Big Data:
• Experience with geospatial Big Data pipelines, analysis techniques, and tools.
• AWS Cloud Experience:
• Familiarity with services like EC2, S3, Lambda, RDS, Glue, etc.
• Leadership & Collaboration:
• Promotes team engagement, psychological safety, and Agile practices such as paired programming, jam sessions, and knowledge sharing.
• Values innovation, experimentation, and rapid prototyping to generate insights.
• Encourages coding best practices, including CI/CD, source control, SDLC, and technical debt management.
• Partners with Product Owners to prioritize the backlog and provides rough estimates in ambiguous situations.
• Interpersonal Skills:
• Demonstrates strong communication, mentoring, problem-solving, and collaboration abilities.
• Education:
• Bachelor's or Master’s in geography, computer science, engineering, mathematics, geosciences, or a related field.

Preferred Qualifications:

• A proven track record of staying current with advancements in geospatial technologies.
• Experience with geospatial analysis and visualization tools and techniques.
• Knowledge of web mapping frameworks (e.g., Deck.gl, Leaflet, MapLibre).
• Familiarity with geospatial and other RESTful APIs.
• Experience with geospatial data processing frameworks like GeoTrellis or Rasterio.
• Awareness of machine learning or AI techniques for geospatial data analysis.
• Familiarity with serverless architectures or Kubernetes for geospatial processing.