Entry Level Geospatial Data Engineer Jobs in Denver, CO

Overview

Do you have a strong analytical foundation and a proven ability to turn complex data into practical solutions?

Join Esri's Professional Services team as a Data Scientist II and help customers solve real-world business challenges through spatial data science and the ArcGIS platform. Working directly with customers and project teams, you will design and implement ArcGIS-centered solutions that combine geospatial analysis, advanced analytics, statistics, and software development to deliver actionable insight and operational value.

Our team specializes in spatial statistics, forecasting, market and demographic analysis, graph analytics, network analysis, and large-scale geospatial analytics. We leverage the full ArcGIS ecosystem, including out-of-the-box capabilities, extensions, configurable workflows, and custom applications, to deliver scalable solutions. Machine learning, deep learning, computer vision, and generative AI are valuable tools that we apply when appropriate, but they are part of a broader analytical toolkit rather than the primary focus of our work.

We're looking for a data scientist who can select the right analytical approach for each challenge and integrate advanced methods into practical ArcGIS-based solutions. If you enjoy solving complex problems, working directly with customers, and helping organizations make better decisions through spatial data science, we'd love to hear from you.

Esri has a Relocation Assistance Program and can provide support with relocating to the Denver, CO area for this position.

Responsibilities

• Work directly with customers to understand business objectives, workflows, and analytical requirements
• Design and implement ArcGIS-centered solutions using ArcGIS Pro, ArcGIS Enterprise, ArcGIS Online, ArcGIS Knowledge, Business Analyst, GeoAnalytics, and related capabilities
• Apply spatial statistics, machine learning, forecasting, graph analytics, network analysis, market analysis, and other advanced analytical methods to solve customer problems
• Configure and extend ArcGIS capabilities through Python, notebooks, APIs, and custom applications
• Develop data processing pipelines and analytical workflows that integrate spatial and nonspatial data from a variety of sources
• Build reusable tools, applications, and solution accelerators that help customers operationalize analytical results
• Create maps, dashboards, reports, and visualizations that communicate findings and support decision-making
• Collaborate with GIS professionals, data engineers, software developers, solution architects, and project managers to deliver successful customer outcomes
• Evaluate emerging analytical techniques and technologies and apply them appropriately within ArcGIS-based solutions

Requirements

• 2+ years of professional experience in data science, analytics, GIS, or a related technical field
• Strong foundation in statistics, mathematics, and analytical problem solving
• Experience developing analytical workflows using Python
• Experience working with ArcGIS software or other geospatial technologies
• Experience preparing, analyzing, and visualizing real-world datasets
• Ability to communicate technical concepts and analytical findings to technical and nontechnical audiences
• Ability to work effectively with customers and cross-functional project teams
• Bachelor's degree in data science, statistics, mathematics, computer science, geography, economics, engineering, or a related quantitative field

Recommended Qualifications

• Master's degree in mathematics, statistics, computer science, physics, or a STEM related field
• Experience with one or more of the following:
  
  • ArcGIS Pro, ArcGIS Enterprise, ArcGIS Online, ArcGIS Knowledge, GeoAnalytics, Business Analyst, Spatial Analyst, Network Analyst, or related ArcGIS technologies
  • Spatial statistics and geospatial analytics
  • Time series analysis and forecasting
  • Market, demographic, and customer analytics
  • Graph analytics, knowledge graphs, or network science
  • Big data and distributed analytics
  • Cloud-native analytics platforms
  • Custom application development, automation, and APIs
  • Deep learning, computer vision, generative AI, or large language models