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Geopandas Jobs (NOW HIRING)

Descartes Labs Government

Sales Engineer

MD · Remote

Common geospatial libraries (GDAL, rasterio, GeoPandas) and viewers (QGIS, Cesium, Mapbox, Kepler.gl) * Notebooks (Jupyter), lightweight web frameworks (Streamlit, Next.js, Flask) * REST/GraphQL APIs ...

RE/SPEC Inc.

Associate GIS Consultant

Denver, CO · On-site

... GeoPandas, Pandas, and Shapely. Understanding of web languages such as JavaScript is beneficial. As a GIS Consultant, you will operate within the Data and Technology Services (DTS) market sector of ...

Xentity Corporation

ArcPy and Python libraries (e.g. gdal, pandas, geopandas, shapley, etc.) * Experience conducting spatial analysis and supporting geospatial data development * Strong communication skills and ability ...

Vantor

Senior Geospatial Data Engineer

Mclean, VA · On-site

$116K - $139K/yr

... ArcPy, GeoPandas, Shapely, Fiona, Rasterio, GDAL, QGIS, or similar geospatial tools and libraries. • Experience building repeatable data pipelines for geospatial data ingestion, processing ...

Stantec

Transportation Analyst (Modeling)

Portland, OR · On-site

Familiarity with GIS tools such as ArcGIS, QGIS, or GeoPandas. * Understanding of travel demand modeling concepts and typical application procedures. * Strong quantitative, analytical, and problem ...

Leidos

Python Software Engineer

Gaithersburg, MD · On-site

Solid understanding of modern Python libraries and frameworks, including GeoPandas, Fiona, Pillow, PyTesseract, and XMLDiff, with experience developing geospatial, document-processing, and data ...

RINA

North America GIS Developer

Chicago, IL

... GeoPandas, or Fiona. - Strong knowledge of databases (e.g., PostgreSQL/PostGIS, SQL Server, Oracle) and spatial queries. - Familiarity with version control tools (e.g., Git, GitHub, Bitbucket ...

Entarian

Scientific Programmer

College Park, MD

$85K - $115K/yr

Basic knowledge of GIS tools (e.g., QGIS, ArcGIS, GeoPandas, raster/terra) * Familiarity with spatial and spatiotemporal modeling techniques * Must be a US Citizen or Permanent Resident and able to ...

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Geopandas information

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

To excel as a Geopandas Data Analyst, you need a solid background in geospatial analysis, Python programming, and data visualization, often supported by a degree in geography, GIS, or data science. Familiarity with Geopandas, Jupyter Notebooks, QGIS, and spatial databases is typically required. Strong problem-solving skills, attention to detail, and effective communication help you interpret complex spatial data and present insights clearly. These competencies are crucial for transforming geographic data into actionable information for decision-makers.

What is GeoPandas?

GeoPandas is an open-source Python library that makes working with geospatial data in Python easier. It extends the popular pandas library to allow spatial operations on geometric types, such as points, lines, and polygons. GeoPandas enables users to perform spatial joins, plot geographic data, and read or write different geographic file formats like Shapefiles and GeoJSON. This library is widely used in fields such as geography, urban planning, and data science for geospatial analysis.

What are some common challenges faced when working with large geospatial datasets in a GeoPandas role?

When working with large geospatial datasets in a GeoPandas-focused role, a common challenge is managing performance and memory usage. GeoPandas is built on top of pandas and shapely, which can struggle with very large or complex geospatial files, leading to slow processing times or even memory errors. Professionals in this role often address these issues by optimizing workflows, using spatial indexing, or integrating GeoPandas with other tools like Dask or PostGIS to handle scalability. Staying up to date with best practices and understanding the limitations of the GeoPandas ecosystem is crucial for efficiently managing large-scale spatial data projects.

What is the difference between Geopandas vs QGIS Developer?

AspectGeopandasQGIS Developer
Required credentialsPython programming, GIS knowledgeGIS certifications, programming skills
Work environmentPython scripts, data analysisDesktop GIS applications, custom plugin development
Employer and industry usageData analysis firms, research institutionsGIS consulting, environmental agencies
Common search and comparison intentData processing, spatial analysisMap creation, GIS application development

Geopandas is primarily used for spatial data analysis and manipulation within Python, ideal for data scientists and analysts. QGIS Developers focus on creating and customizing GIS applications using QGIS software. While both roles involve GIS, Geopandas emphasizes scripting and data analysis, whereas QGIS Developers work on application development and interface customization.

More about Geopandas jobs
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What states have the most Geopandas jobs? States with the most job openings for Geopandas jobs include:
What job categories do people searching Geopandas jobs look for? The top searched job categories for Geopandas jobs are:
Geopandas jobs near you
Infographic showing various Geopandas job openings in the United States as of July 2026, with employment types broken down into 98% Full Time, and 2% Contract. Highlights an 78% Physical, 5% Hybrid, and 17% Remote job distribution.
Senior Data Engineer

Senior Data Engineer

Intercontinental Exchange Holdings, Inc.

Atlanta, GA • On-site

$101K - $138K/yr

Full-time

Re-posted 2 days ago

Job description

Overview
Job Purpose
ICE Data Services (an Intercontinental Exchange company) is seeking a Senior Data Engineer to join its Data Impact & Innovation team. This team supports a variety of reference data, index, climate finance, and alternative data products. The role contributes to the data platforms and pipelines that help the financial sector understand and respond to carbon transition risk, physical risk, and related challenges.
Our team maintains a global-scale geospatial data platform in Google BigQuery, holding many terabytes of data across carbon transition risk, physical climate risk, and social/demographic features - feeding analytical products for fixed income and real estate financial instruments, supporting the ambitious product roadmap for ICE Climate and other data products. Our engineering stack includes:
  • Orchestration: Airflow, moving toward composable task abstractions over a shared pipeline framework
  • Transformation: dbt, and other data lineage and DQA tools, primarily using Google BigQuery
  • Geospatial processing: Python (GeoPandas, Shapely, GeoAlchemy2 against PostGIS) for vector operations, and R
  • Execution and compute environments: Hybrid across Google Cloud Platform and on-premise RHEL Linux infrastructure
  • Ingestion: Third-party vendor feeds via API, SFTP, cloud storage, and database replication

Typical engineering challenges include working with data science and climate science teams in operationalizing trained models and data pipelines, absorbing upstream vendor corrections and historical restatements without corrupting downstream artifacts, scaling raster x vector joins at terabyte scale, evolving schemas and spatial-indexing strategies as data sources broaden, and balancing long-running batch workflows against emerging sub-daily refresh cadences.
Responsibilities
  • Take significant components of the data platform from "works" to "mature" - tightening reliability, observability, cost/performance characteristics, and operational discipline across our ingestion, transformation, and serving layers.
  • Establish and foster adoption of technical standards for the team's work - including Airflow DAG structure, dbt model layout, BigQuery schema and partitioning conventions, pipeline testing practices, and deployment workflows.
  • Lead technical design discussions, mentor other data engineers through code review, pairing, and design-doc review, and grow them along their career path.
  • Act as a technical point of contact for cross-functional initiatives - partnering with data science, climate science, product, and infrastructure colleagues to drive forward decisions and make tradeoffs explicit.
  • Deliver day-to-day work across the stack above - authoring Airflow DAGs and dbt models, contributing geospatial processing capabilities, and shipping cleanly partitioned, audit-friendly outputs from ingestion through serving.
  • Support data science and climate science teams by helping design the tooling, training, and validation environments, and by deploying their trained models into production.
  • Effectively leverage AI and LLM-based developer tooling to accelerate development workflows and improve code quality.
  • Identify opportunities to improve and optimize data pipelines - for speed, cost, robustness, integrity, and operational simplicity.
  • Work with business analysts, product management, and adjacent engineering teams to understand and refine new data requirements.

Knowledge and Experience
  • 5+ years of professional experience as a data engineer, with a track record of architecting, shipping, and operating production data pipelines end-to-end.
  • Experience mentoring and developing other data engineers - through code review, pairing, design discussions, and career coaching.
  • Ability to establish and foster adoption of technical standards.
  • A habit of actively monitoring, evaluating, and prototyping emerging big-data, geospatial, and machine-learning technologies and platforms - staying conversant in advances across cloud data engines, geospatial libraries and standards, and ML/MLOps frameworks - and bringing the most promising into the team's design discussions, evaluations, and adoption decisions.
  • Strong system-design judgment across the tradeoff space of performance, cost, maintainability, and auditability
  • Comfort scoping, decomposing, and delegating work for other engineers.
  • Strong written and verbal communication - able to translate technical tradeoffs for senior business, product, and client stakeholders.
  • Deep fluency in modern, typed Python as a primary working language, including comfort with type-driven design (e.g. Pydantic v2).
  • Strong SQL background, including experience partitioning, clustering, and performance-tuning queries on modern cloud warehouses - Google BigQuery experience strongly preferred.
  • Production experience with dbt for managing warehouse transformations, and with Airflow (or a comparable orchestrator) for workflow orchestration.
  • Solid grounding in geospatial data engineering - Python tooling (GeoPandas, Shapely), spatial databases (PostGIS), raster processing, or adjacent skills.
  • A systems-thinking orientation: anticipates cascading effects of upstream data changes, schema evolution, and vendor corrections; designs pipelines with observability, auditability, and graceful failure in mind.
  • Comfort owning production incidents and debugging distributed systems.
  • Experience working cooperatively with systems, network, and infrastructure engineering and operations teams to ensure proper monitoring, alerting, and incident response workflows.
  • Demonstrated ability to integrate AI/LLM coding assistants productively - treating them as a force multiplier rather than a substitute for judgment.
  • Curiosity about the financial and climate/geospatial domains and contexts the team operates in.

Preferred Knowledge and Experience
  • Well-versed in and opinionated about the modern Python ecosystem.
  • Exposure to columnar and lakehouse technologies (Parquet, ClickHouse, DuckDB).
  • Working understanding of data lineage, data quality validation, and metadata/cataloging frameworks.
  • Prior experience in a hybrid cloud + on-premise environment, and with full software development lifecycle (SDLC) best practices and processes.
  • Prior exposure to ML deployment workflows - supporting data science teams with training tooling and/or model-serving infrastructure.
  • Familiarity with R, particularly geospatial packages.

#LI-HR1 #LI-ONSITE
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Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.

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