Scientific Data Engineering Specialist
$77,600–$176,000 year
On-siteMcLean, Virginia, United States
Job Summary
Design and implement scalable data ingestion, storage, cataloging, and API capabilities for high-volume, secure scientific datasets. Lead the development of event-driven microservices that process real-time environmental data, including satellite imagery, radar, and model output. Evolve legacy data flows into modern cloud-native architectures while strengthening platform observability and metadata systems. Build end-to-end workflows covering ingestion, decoding, transformation, georeferencing, and synchronized distribution across teams. Partner with engineering peers to drive technical direction, validate performance, and support mission-critical systems requiring strict reliability.
Required Qualifications
- U.S. citizenship
- Public Trust or Suitability/Fitness determination
- 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 meteorological products
- Experience architecting concurrent, performance-critical systems capable of handling large numerical and geospatial workloads using Java, Spring Boot, and cloud technologies
- Ability to build end-to-end scientific data workflows, including ingestion, decoding, transformation, georeferencing, cataloging, and synchronized distribution across microservices
- HS diploma or GED
Desired Qualifications
- 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 telemetry, 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
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