Scientific Data Engineering Specialist

Booz Allen Hamilton•McLean, VA

3d•$77,600 - $176,000•Remote

About The Position

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, metrics, and logging. Build and refine API and notification services to streamline data exchange and integration. Enhance metadata 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. citizenship is required.

Requirements

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
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
U.S. citizenship is required

Nice To Haves

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

Responsibilities

Provide technical leadership in designing and implementing scalable data ingestion, storage, cataloging, API, and processing capabilities.
Ensure high‑volume, secure, and reliable data pipelines operate effectively.
Contribute to modern cloud‑based architectures.
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.
Strengthen platform observability, metrics, and logging.
Build and refine API and notification services to streamline data exchange and integration.
Enhance metadata 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.