Scientific Data Engineering Specialist

Booz Allen Hamilton•McLean, VA

3d•$77,600 - $176,000•Hybrid

About The Position

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 across internal data teams. Contribute to modern cloud-based architectures and guide development and optimization of microservices for ingestion, transformation, and advanced processing workflows. Partner 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 quality practices including test-driven development, integration testing, and performance validation. Participate in sprint activities, architectural discussions, and cross-team collaboration to drive clarity, alignment, and technical direction. U.S. citizenship is required due to the nature of the work. Applicants selected will be subject to a government investigation and may need to meet eligibility requirements of the U.S. government client.

Requirements

Experience with geospatial data processing using PostgreSQL, PostGIS, spatial queries, coordinate reference systems, spatial envelopes, and georeferenced data.
Experience processing scientific data formats such as GOES-R, NEXRAD, GRIB2, SYNOPTIC, or polar-orbiter.
Experience designing and implementing Java- and Spring-based microservices using technologies such as Spring Boot, Spring Cloud Gateway, Spring Security, WebFlux, JPA, and Hibernate.
Experience building end-to-end scientific data workflows, including ingestion, decoding, transformation, georeferencing, storage, cataloging, and synchronized distribution across microservices.
Experience developing scientific visualization capabilities, including real-time imagery access, interactive mapping, ArcGIS integrations, plotting, or meteorological visualizations.
Experience engineering cloud-native scientific data systems supporting high-volume environmental, observational, weather, or geospatial data.
Experience developing event-driven data pipelines using messaging and change-data-capture technologies such as Apache Pulsar, Kafka, and Debezium.
Experience defining system architecture, developing technical strategies, and presenting technical direction to senior government or program stakeholders.
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 modernizing legacy scientific or government systems into decoupled, cloud-native architectures.
Experience with AWS services such as EKS, S3, SQS, SNS, IAM, EC2, and ElastiCache.
Experience with Kubernetes, Docker, Helm, Jenkins, GitLab CI/CD, OpenShift, Rancher, or Terraform.
Experience with scientific or operational data stores and caching technologies such as MongoDB, Redis, PostgreSQL, and PostGIS.
Experience with Java virtual threads, structured concurrency, reactive systems, or other techniques for processing large numbers of concurrent files or requests.
Experience with automated quality and security tooling such as JUnit, Mockito, Testcontainers, LocalStack, SonarQube, or container security scanning.
Experience supporting meteorological, satellite, radar, atmospheric-science, or other environmental-data missions.
Experience collaborating with government stakeholders, scientific users, engineering teams, DevOps teams, or quality-assurance organizations.
Experience mentoring developers, supporting onboarding, or establishing shared engineering standards and reusable libraries.

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 across internal data teams.
Contribute to cloud-based architectures and guide development and optimization of microservices supporting ingestion, transformation, and advanced processing workflows.
Collaborate with engineering peers to modernize legacy data flows into event-driven systems and improve platform observability, metrics, and logging.
Build and refine API and notification services for data exchange and integration.
Enhance metadata and catalog systems to improve data discoverability and synchronization with downstream users.
Support test-driven development, integration testing, and performance validation.
Participate in sprint activities, architectural discussions, and cross-team collaboration to drive clarity, alignment, and technical direction.