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Shapely Jobs in Missouri (NOW HIRING)

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Shapely information

What is a Shapely job?

Shapely jobs typically refer to roles that involve working with the Shapely library, a popular Python package used for manipulation and analysis of planar geometric objects. Professionals in these positions often use Shapely for tasks in geographic information systems (GIS), spatial data analysis, and mapping. Job responsibilities may include developing geospatial applications, processing and analyzing spatial data, and integrating Shapely with other Python libraries such as GeoPandas. These roles are commonly found in industries like urban planning, environmental science, and data analytics. Having strong Python skills and experience with spatial data is usually required.

What are the key skills and qualifications needed to thrive as a Shapely web developer?

To thrive as a Shapely web developer, you need proficiency in web development languages such as HTML, CSS, JavaScript, and familiarity with WordPress, alongside experience with responsive design principles. Knowledge of the Shapely WordPress theme, page builders, plugins, and version control systems like Git is typically required. Strong problem-solving abilities, attention to detail, and effective communication help developers efficiently address client needs and collaborate with teams. These skills ensure the development of attractive, functional, and user-friendly websites that meet client specifications.

What are some common challenges faced by software engineers working on the Shapely library, and how can job seekers prepare for them?

Software engineers contributing to the Shapely library often encounter challenges related to geometric algorithms, spatial data precision, and cross-platform compatibility. Debugging complex geometry issues and ensuring robust handling of edge cases require a solid understanding of computational geometry and Python development. Collaboration with GIS professionals and open-source contributors is also a key part of the role, so strong communication skills and experience with open-source workflows are valuable. Familiarity with related libraries like GEOS and understanding spatial data formats can help job seekers excel.

What is the difference between Shapely vs GIS Analyst?

AspectShapelyGIS Analyst
Required CredentialsBasic programming knowledge, Python skillsBachelor's degree in Geography, GIS, or related field
Work EnvironmentProgramming, data analysis, scriptingMapping, spatial data analysis, report creation
Industry UsageUsed in GIS software development and data processingApplied in urban planning, environmental management, and more
Common Search IntentData manipulation, spatial analysis in PythonGIS data analysis, mapping projects

Shapely is a Python library for geometric operations and spatial data manipulation, often used by developers and data analysts. GIS Analysts perform broader spatial data analysis, mapping, and reporting within GIS software. While Shapely focuses on geometric calculations, GIS Analysts handle comprehensive spatial projects. Both roles overlap in spatial data handling but differ in scope and tools used.

What are popular job titles related to Shapely jobs in Missouri?

For Shapely jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Shapely jobs in Missouri look for?

The top searched job categories for Shapely jobs in Missouri are:

What cities in Missouri are hiring for Shapely jobs?

Cities in Missouri with the most Shapely job openings:

Shapely jobs near you
Infographic showing various Shapely job openings in Missouri as of August 2026, with employment types broken down into 84% Full Time, 8% Part Time, and 8% Contract. Highlights an 65% Physical, and 35% Remote job distribution.

Senior Geospatial Data Engineer

Object Computing, Inc.

Saint Louis, MO • On-site

$103K - $140K/yr

Full-time

Re-posted 18 days ago

Job description

Object Computing, Inc. is seeking a Senior Geospatial Data Engineer to join our Xtrack Product Team. In this role, you will lead the design and implementation of scalable, cloud-based geospatial data infrastructures, and play a key part in shaping our data architecture and product engineering strategy with a focus on improving safety and operational efficiencies for organizations in the rail industry. You will work with cutting-edge technologies in image processing, artificial intelligence, cloud computing, and geospatial database management. Your work will optimize complex business processes and unlock new value from large-scale geospatial datasets.
What you will do:
  • Architect, design, and maintain robust, scalable data pipelines and infrastructures for geospatial and big data applications maintaining a focus on performance and the ultimate end-user product experience.
  • Lead the development and optimization of ETL processes for ingesting, cleaning, transforming, and storing large volumes of geospatial and tabular data.
  • Design, build, and interact with API-driven, service-to-service web services (using FastAPI, Litestar, Flask, etc.) to enable integration across a suite of products.
  • Collaborate with backend and platform engineers to ensure secure, reliable, and scalable service-to-service communication.
  • Translate complex analytics and business questions into actionable, production-grade data solutions.
  • Collaborate closely with data scientists, analysts, and business stakeholders to deliver high-impact data products.
  • Drive the adoption and optimization of cloud-based data solutions (e.g., GCP, AWS, Azure).
  • Ensure data quality, integrity, and security across all stages of the data lifecycle.
  • Mentor and provide technical guidance to junior data engineers and team members.
  • Communicate technical details and insights clearly to both technical and non-technical audiences, including leadership.
  • Proactively recommend and implement improvements to existing data infrastructure and software programs.
  • Stay current with industry trends and emerging technologies in geospatial data engineering.
What you will bring:
  • An excitement and dedication towards manifesting real and measurable impact for customers and clients and a dedication to being a team player towards achievement of those outcomes.
  • Experience in software development, data engineering, or big data roles, preferably with a focus on geospatial data.
  • Experience building solutions with Python.
  • Experience with relational databases (e.g., SQL), including advanced query building, data extraction, and manipulation.
  • Experience architecting and optimizing cloud-based data solutions (preferably GCP, AWS, or Azure).
  • Deep experience with big data technologies such as Hadoop, Spark, MapReduce, or Kafka.
  • Experience integrating with API-driven, service-to-service web services.
  • Demonstrated ability to lead projects, mentor team members, and drive technical decisions.
  • Strong problem-solving skills, resourcefulness, and ability to work independently or collaboratively.
  • Excellent organizational, interpersonal, and communication skills.
What will make you stand out:
  • Expertise with geospatial libraries and tools (e.g., GDAL, PDAL, PostGIS, GeoPandas, Shapely).
  • Experience deploying and scaling machine learning (ML) models/algorithms in production.
  • Strong experience with geospatial analytics and working with geospatial data formats (e.g., LAS, LAZ, COPC, GeoTIFF, Shapefiles).
  • Experience leading teams in integrating and scaling complex ML/Deep Learning (DL) algorithms.
  • Experience working with LiDAR data and deriving real-world insights from point clouds.
  • Experience with ESRI products (ArcGIS Pro, ArcGIS Online, ArcGIS Enterprise) or other GIS platforms.
  • Experience with data streaming, real-time data processing, or cloud-native geospatial solutions.
  • Cloud certifications (e.g., Google Cloud Professional Data Engineer, AWS Certified Data Analytics).
  • Experience with OAuth, authentication, and API key management for secure service-to-service communication.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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