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Contract Geospatial Data Engineer Jobs in Georgia

Visionaire Partners

GIS Data Analyst

Atlanta, GA · On-site

This is focused on the engineering side of the business! You will be able to gain experience in ... You will also directly perform geospatial data preparation and assemblage tasks.This technical role ...

SOSi

Geospatial Analyst Expert

Augusta, GA

The role involves providing advanced geospatial analysis, data integration, and technical ... The Government reserves the right to require contract performance at alternate locations, as ...

SOSi

Geospatial Analyst Expert

Augusta, GA · On-site

The role involves providing advanced geospatial analysis, data integration, and technical ... The Government reserves the right to require contract performance at alternate locations, as ...

SOSi

Geospatial Analyst Expert

Fort Eisenhower, GA · On-site

The role involves providing advanced geospatial analysis, data integration, and technical ... The Government reserves the right to require contract performance at alternate locations, as ...

Prescient Edge

SME- Geospatial Analyst

Augusta, GA · On-site

Please note that the availability of this position is contingent upon contract award. Benefits: At ... Extensive experience with geographically derived assessments, enterprise data integration, spatial ...

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Contract Geospatial Data Engineer information

What are the key skills and qualifications needed to thrive as a Contract Geospatial Data Engineer, and why are they important?

To thrive as a Contract Geospatial Data Engineer, you need expertise in GIS principles, spatial analysis, data modeling, and proficiency with languages like Python or SQL, typically supported by a degree in geography, computer science, or a related field. Familiarity with technologies such as ESRI ArcGIS, QGIS, remote sensing platforms, and cloud-based geospatial systems, as well as certifications like GISP, is often required. Strong problem-solving, attention to detail, and effective communication skills help you interpret complex data and collaborate across project teams. These skills ensure accurate, efficient handling of geospatial data crucial for informed decision-making in diverse industries.

What is the difference between Contract Geospatial Data Engineer vs GIS Analyst?

AspectContract Geospatial Data EngineerGIS Analyst
Required CredentialsBachelor's in GIS, Geography, Computer Science; experience with GIS software and programmingBachelor's in Geography, GIS, or related field; proficiency in GIS tools
Work EnvironmentProject-based, technical, often remote or on-siteOffice or fieldwork, data analysis, map creation
Employer & Industry UsageTech firms, government agencies, environmental companiesUrban planning, environmental agencies, consulting firms

The Contract Geospatial Data Engineer focuses on building and maintaining GIS data systems, often requiring programming skills, while a GIS Analyst primarily analyzes spatial data and creates maps. Both roles are essential in GIS projects but differ in technical depth and responsibilities.

What are Contract Geospatial Data Engineers?

Contract Geospatial Data Engineers are professionals who specialize in managing, analyzing, and visualizing spatial data on a temporary or project-based contract. They use geographic information systems (GIS), remote sensing, and data engineering tools to process location-based data for various industries such as urban planning, environmental science, or logistics. Unlike full-time employees, contract engineers typically work for a set duration or on specific projects, offering flexibility to employers and a variety of work for the engineer. Their expertise helps organizations make data-driven decisions based on spatial analysis.

What are some common challenges faced by Contract Geospatial Data Engineers when working with diverse datasets from multiple sources?

Contract Geospatial Data Engineers often encounter challenges related to data integration and quality control, as datasets can come in various formats, projections, and levels of accuracy. Ensuring compatibility and consistency across sources requires strong attention to detail and proficiency with geospatial tools such as GIS software and scripting languages. Additionally, contractors must quickly adapt to the unique workflows and expectations of different clients or teams, making effective communication and project management skills essential. These challenges are balanced by the opportunity to work on a variety of projects and expand expertise in different geospatial domains.
What are the most commonly searched types of Geospatial Data Engineer jobs in Georgia? The most popular types of Geospatial Data Engineer jobs in Georgia are:
What are popular job titles related to Contract Geospatial Data Engineer jobs in Georgia? For Contract Geospatial Data Engineer jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Contract Geospatial Data Engineer jobs in Georgia look for? The top searched job categories for Contract Geospatial Data Engineer jobs in Georgia are:
What cities in Georgia are hiring for Contract Geospatial Data Engineer jobs? Cities in Georgia with the most Contract Geospatial Data Engineer job openings:
Contract Geospatial Data Engineer jobs near you
Senior Data Engineer

Senior Data Engineer

Intercontinental Exchange Holdings, Inc.

Atlanta, GA • On-site

$101K - $138K/yr

Full-time

Posted 15 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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