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# Scientific Data Engineering Specialist

- **Company:** Booz Allen Hamilton Inc.
- **Location:** Fairfax, United States
- **Salary:** $77,600.0 - $176,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Geographic Information Systems, Application Programming Interfaces (APIs), Amazon S3, ArcGIS (Software), Big Data, Cloud Computing, Information Engineering, Data Systems, Electronic Data Interchange (EDI), Identity and Access Management, Python (Programming Language), MongoDB, NoSQL, PostGIS, Redis, Data Streaming, Systems Architecture, WebGL, Data Logging, Performance Testing, Test-Driven Development (TDD), Data Ingestion, Spring-boot, Event Driven Architecture, Integration Tests, Apache Kafka, Decoding, Amazon Simple Queue Service (SQS), Data Pipelines, AWS EKS, Microservices
- **Published:** August 15, 2026
- **Apply:** https://www.dice.com/job-detail/2c759754-3522-4954-8ae7-3aff8b82e599

## About the Role

* Experience with geospatial data processing using tools such as PostGIS, advanced coordinate reference systems, spatial envelopes, and scientific imagery formats such as GOES-R, NEXRAD, and polar-orbiter products
 * Experience engineering cloud-native scientific data systems supporting high-volume environmental, observational, or geospatial data
 * Experience designing and implementing high-throughput microservices and event-driven pipelines that process real-time scientific datasets, including satellite imagery, radar data, and model output
 * Experience developing tooling for scientific visualization, including real-time imagery rendering, spatial mapping, and interactive met eorological products
 * Experience architecting concurrent, performance-critical systems capable of handling large numerical and geospatial workloads using Java, Spring Boot, and cloud technologies
 * Experience defining system architecture, writing technical strategies, and presenting scientific or technical program direction to senior stakeholders
 * Ability to build end-to-end scientific data workflows, including ingestion, decoding, transformation, georeferencing, cataloging, and synchronized distribution across microservices
 * Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
 * Bachelor's degree

Nice If You Have:

 * Experience with scientific programming languages or frameworks such as Python, geospatial libraries, scientific image decoders, or spatial analytics tools
 * Experience with distributed scientific data systems such as Kafka, Pulsar, or other messaging tools used in real-time environmental data dissemination
 * Experience with cloud infrastructure for scientific workloads, including AWS EKS, S3, SQS / SNS, IAM, and container-based orchestration for large data pipelines
 * Experience with satellite, radar, or atmospheric science domains, including handling raw instrument data, encrypted tele met ry, or environmental sensor outputs
 * Experience with NoSQL or scientific storage patterns, including MongoDB, Redis, or other high-performance caching systems suitable for scientific applications
 * Experience with scientific visualization frameworks, WebGL-based rendering, map services, or ArcGIS integrations supporting operational users
 * Experience supporting mission-critical systems with strict performance, reliability, and concurrency requirements common in scientific and environmental operations
 * Experience collaborating with scientific end-users such as forecasters, analysts, or research teams, to translate mission needs into technical designs

## Description

Provide technical leadership in designing and implementing scalable data ingestion, storage, cataloging, API, and processing capabilities as a Scientific Data Engineering Specialist. Ensure that high-volume, secure, and reliable data pipelines operate effectively across various internal data teams. Contribute to modern cloud-based architectures and guide the development and optimization of microservices that support ingestion, transformation, and advanced processing workflows. Partner closely with engineering peers to evolve legacy data flows into event-driven systems and strengthen platform observability, met rics, and logging. Build and refine API and notification services to streamline data exchange and integration. Enhance met adata and catalog systems to improve data discoverability and synchronization with downstream users. Support strong quality practices, including test-driven development, integration testing, and performance validation. Participate actively in sprint activities,
architectural discussions, and cross-team collaboration to help drive clarity, alignment, and technical direction. Work with us to use data for good. Due to the nature of work performed within this facility, U.S. citizen ship is required .


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