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Geopandas Python Jobs (NOW HIRING)

Automate data processing and analysis workflows using Python, SQL, and Excel. * Collaborate with ... Familiarity with GIS tools such as ArcGIS, QGIS, or GeoPandas. * Understanding of travel demand ...

... in Python for data analysis, automation, and workflow development - Experience with data ... Shapely, GeoPandas, Kibana, Elastic, SQL, Jupyter) - Experience with ETL workflows, batch ...

CPCI v7.0 Python Upgrade / Code Migration * Lead and/or support the migration of CPC International ... Basic knowledge of GIS tools (e.g., QGIS, ArcGIS, GeoPandas, raster/terra) * Familiarity with ...

Demonstrated experience performing geospatial analytics in Python, using GeoPandas or equivalent geospatial tools and libraries. * Comprehensive knowledge and experience in developing data-driven ...

CPCI v7.0 Python Upgrade / Code Migration * Lead and/or support the migration of CPC International ... Basic knowledge of GIS tools (e.g., QGIS, ArcGIS, GeoPandas, raster/terra) * Familiarity with ...

Senior GenAI Engineer

Arlington, VA ยท On-site

$120K - $165K/yr

Familiarity or experience with Python libraries/pkgs such as pandas, NumPy, GeoPandas, requests, urllib3, Matplotlib, plotly, scikit-learn, sciPy, OpenCV, GDAL, Keras, PyTorch, TensorFlow, NLTK ...

Familiarity or experience with Python libraries/pkgs such as pandas, NumPy, GeoPandas, requests, urllib3, Matplotlib, plotly, scikit-learn, sciPy, OpenCV, GDAL, Keras, PyTorch, TensorFlow, NLTK ...

Scientific Programmer

College Park, MD ยท On-site

$85K - $115K/yr

CPCI v7.0 Python Upgrade / Code Migration * Lead and/or support the migration of CPC International ... Basic knowledge of GIS tools (e.g., QGIS, ArcGIS, GeoPandas, raster/terra) * Familiarity with ...

Automate data processing and analysis workflows using Python, SQL, and Excel. * Collaborate with ... Familiarity with GIS tools such as ArcGIS, QGIS, or GeoPandas. * Understanding of travel demand ...

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Geopandas Python information

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How much do geopandas python jobs pay per hour?

As of Jun 22, 2026, the average hourly pay for geopandas python in the United States is $58.62, according to ZipRecruiter salary data. Most workers in this role earn between $48.32 and $66.59 per hour, depending on experience, location, and employer.

What is GeoPandas in Python?

GeoPandas is an open-source Python library that simplifies working with geospatial data. It extends the popular Pandas library to enable spatial operations on geometric types, such as points, lines, and polygons. With GeoPandas, users can easily read, write, and manipulate geographic data formats like Shapefile, GeoJSON, and others. It integrates well with other libraries, such as Matplotlib for visualization and Shapely for advanced geometric operations, making it a powerful tool for geographic data analysis in Python.

What are GeoPandas used for in Python?

GeoPandas is a Python library used by geospatial data analysts and GIS professionals to simplify working with geographic data. It extends the pandas library to include spatial operations, enabling users to read, write, analyze, and visualize geospatial data such as shapefiles and GeoJSON files efficiently.

What is the difference between Geopandas Python vs GIS Analyst?

AspectGeopandas PythonGIS Analyst
Required CredentialsPython programming, GIS fundamentalsGIS certifications, degree in geography or related field
Work EnvironmentData analysis, scripting, codingMap creation, spatial data management, report generation
Industry UsageData science, software development, geospatial analysisUrban planning, environmental management, government agencies

Geopandas Python focuses on spatial data analysis using Python programming, ideal for data scientists and developers. GIS Analysts work with spatial data in various industries, often using GIS software and tools. While both roles involve geospatial data, Geopandas Python emphasizes coding and automation, whereas GIS Analysts focus on data management and visualization.

What are the key skills and qualifications needed to thrive as a Geopandas Python Developer, and why are they important?

To thrive as a Geopandas Python Developer, you need a solid foundation in Python programming, geospatial data analysis, and GIS concepts, often backed by a degree in computer science or geography. Familiarity with Geopandas, Shapely, Fiona, and other geospatial libraries, as well as experience with spatial databases and data visualization tools, is typically required. Strong problem-solving skills, attention to detail, and effective communication help you deliver robust geospatial solutions and collaborate with team members. These skills are critical for accurately processing spatial data, generating valuable insights, and supporting data-driven decision-making in various industries.

What are some common challenges Geopandas Python developers face when working with large geospatial datasets?

Geopandas Python developers often encounter performance limitations when processing very large geospatial datasets, as Geopandas is built on top of Pandas and can be memory-intensive. Handling operations like spatial joins, dissolves, or aggregations on millions of features may lead to slowdowns or memory errors. To address these challenges, developers frequently use techniques such as chunking data, leveraging Dask for parallel computing, or integrating with more scalable libraries like PostGIS or PyGEOS. Collaborating closely with data engineers and GIS specialists can also help optimize workflows and ensure efficient data processing.

What is the highest paying Python job?

The highest paying Python jobs are typically senior roles such as Machine Learning Engineer, Data Scientist, or Software Architect, often requiring advanced skills in AI, data analysis, and cloud computing. Salaries can exceed $150,000 annually, especially in industries like finance, technology, and consulting, with experience and certifications further increasing earning potential.

What are the most in demand jobs in Python?

In Python, the most in-demand jobs include data scientist, software developer, machine learning engineer, and data analyst. These roles often require skills in libraries like Pandas and GeoPandas, along with knowledge of data visualization, algorithms, and sometimes geographic information systems (GIS). Proficiency in Python programming and relevant frameworks increases employability in these fields.

Are Python coders still in demand?

Python coders, including those working with Geopandas and other data analysis libraries, are in high demand due to the language's versatility in data science, automation, and web development. Skills in Python, along with knowledge of relevant tools and frameworks, continue to be valuable across many industries and roles.
More about Geopandas Python jobs
What cities are hiring for Geopandas Python jobs? Cities with the most Geopandas Python job openings:
What states have the most Geopandas Python jobs? States with the most job openings for Geopandas Python jobs include:
What job categories do people searching Geopandas Python jobs look for? The top searched job categories for Geopandas Python jobs are:
Infographic showing various Geopandas Python job openings in the United States as of June 2026, with employment types broken down into 43% Part Time, and 57% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $121,932 per year, or $58.6 per hour.

Software Engineer (Full Stack) - SME

GRVTY

Springfield, VA โ€ข Remote

Other

Posted 27 days ago


Job description

What Impact You'll Have:

Join a mission-focused team where your work directly supports critical national security objectives. We are seeking a Subject Matter Expert (SME) Full Stack Developer to lead the design, development, and delivery of scalable, mission-driven applications within an ML/Ops environment. This role combines deep technical expertise with advanced system-level thinking and close collaboration across engineering, data science, and customer stakeholder teams.

The Full Stack Developer will perform rapid application design, ETL, data analysis, and interpretation while developing rules and methodologies for data collection and analysis. You will architect, develop, and maintain a Python-based data warehouse processing system that serves as the backend for a user-facing application, while also leading development of modern GUI applications using REST APIs and contemporary web frameworks.

You will work closely with data scientists, computer vision engineers, ETL engineers, and intelligence analysts to integrate machine learning capabilities into production systems, enabling scalable model deployment, monitoring, and continuous improvement. This role emphasizes ownership, technical leadership, and delivery of production-ready solutions that operate reliably in dynamic, real-world environments.

What You'll Be Owning:

Lead and participate in the architectural design of complex features early in the development lifecycle.
Translate customer requirements and roadmap priorities into technical solutions, tasks, timelines, and resource plans.
Develop, integrate, and maintain full stack applications supporting ML/Ops pipelines and data-driven systems.
Design and implement scalable APIs and services to support machine learning model deployment and inference.
Develop and maintain data pipelines, ETL processes, and data storage solutions for large-scale datasets.
Collaborate with data scientists and ML engineers to operationalize models within production environments.
Optimize application and system performance for scalability, reliability, and efficiency, including edge and distributed environments when applicable.
Conduct peer reviews and establish coding standards to improve overall code quality and maintainability.
Guide development testing, exploratory testing, automated testing, and validation strategies.
Own code in production environments, respond to incidents, and lead root cause analysis and continuous improvement efforts.
Ensure security, compliance, and governance are maintained throughout the development lifecycle.
Perform technical planning, system integration, verification and validation, and risk assessments across system components.
Mentor and develop junior and mid-level engineers, fostering technical growth and high-performing teams.
Drive adoption of modern ML/Ops practices, tools, and automation frameworks across the team.

What You Must Have:

Active TS/SCI clearance with the ability to obtain a CI poly
Bachelor's degree in Computer Science, Engineering, or a related technical field.
14+ years of professional experience in full stack software development.
Expert-level proficiency in Python and object-oriented design patterns.
Extensive experience developing backend systems, APIs, and data processing pipelines.
Strong experience with modern web development frameworks, including React.js, Node.js, and/or Electron.
Deep understanding of data modeling techniques and experience working with large-scale and time series datasets.
Experience with relational and non-relational databases such as PostgreSQL, MongoDB, and BigQuery.
Experience building and maintaining RESTful APIs and microservices architectures.
Experience supporting machine learning workflows, including model integration, deployment, and monitoring.
Familiarity with ML/Ops tools and utilities such as MLflow, DVC, and/or Optuna.
Strong experience with Python libraries such as NumPy and Pandas.
Experience with Python web frameworks such as Flask, FastAPI, Pydantic, Gunicorn, and Uvicorn.
Experience with containerization and DevOps practices, including Docker and CI/CD pipelines.
Experience with web servers such as Apache and Nginx.
Experience working within Agile development environments and using associated tools.

What Would be Nice to Have:

Experience supporting government or defense-related programs.
Experience integrating computer vision or machine learning capabilities into operational systems.
Knowledge of real-time data processing, streaming architectures, or distributed systems.
Experience with cloud-based ML/Ops environments and infrastructure (AWS, Azure, or Google Cloud Platform).
Experience with parallelization and multiprocessing frameworks such as Dask.
Knowledge of geospatial data processing tools and libraries including GeoPandas, Shapely, Rasterio, QGIS, and ArcPy.
Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
Experience with remote procedure call technologies such as gRPC and JSON-RPC.

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