Scientific Data Engineering Specialist

About the Role

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.

Key Responsibilities

• Design and implement 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 for ingestion, transformation, and processing workflows.
• Evolve legacy data flows into event-driven systems.
• Strengthen platform observability, metrics, and logging.
• Build and refine API and notification services.
• Enhance metadata and catalog systems.
• Support quality practices: test-driven development, integration testing, and performance validation.
• Participate in sprint activities, architectural discussions, and cross-team collaboration.

Requirements & Tech Stack

• Geospatial data processing using PostgreSQL, PostGIS, spatial queries, coordinate reference systems, spatial envelopes, and georeferenced data.
• Processing scientific data formats such as GOES-R, NEXRAD, GRIB2, SYNOPTIC, or polar-orbiter.
• Designing and implementing Java- and Spring-based microservices using technologies like Spring Boot, Spring Cloud Gateway, Spring Security, WebFlux, JPA, and Hibernate.
• Building end-to-end scientific data workflows: ingestion, decoding, transformation, georeferencing, storage, cataloging, and synchronized distribution across microservices.
• Developing scientific visualization capabilities: real-time imagery access, interactive mapping, ArcGIS integrations, plotting, or meteorological visualizations.
• Engineering cloud-native scientific data systems supporting high-volume environmental, observational, weather, or geospatial data.
• Developing event-driven data pipelines using messaging and change-data-capture technologies such as Apache Pulsar, Kafka, and Debezium.
• 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.

Qualifications

• Bachelor's degree.

Nice to Have

• Modernizing legacy scientific or government systems into decoupled, cloud-native architectures.
• AWS services such as EKS, S3, SQS, SNS, IAM, EC2, and ElastiCache.
• Kubernetes, Docker, Helm, Jenkins, GitLab CI/CD, OpenShift, Rancher, or Terraform.
• Scientific or operational data stores and caching technologies such as MongoDB, Redis, PostgreSQL, and PostGIS.
• Java virtual threads, structured concurrency, reactive systems, or other techniques for processing large numbers of concurrent files or requests.
• Automated quality and security tooling such as JUnit, Mockito, Testcontainers, LocalStack, SonarQube, or container security scanning.
• Supporting meteorological, satellite, radar, atmospheric-science, or other environmental-data missions.
• Collaborating with government stakeholders, scientific users, engineering teams, DevOps teams, or quality-assurance organizations.
• Mentoring developers, supporting onboarding, or establishing shared engineering standards and reusable libraries.

Vetting

• U.S. citizenship is required.
• Applicants selected will be subject to a government investigation and may need to meet eligibility requirements.

Compensation

• Projected compensation range: $77,600.00 to $176,000.00 (annualized USD).