Data Platform Engineer IV

The remote Data Platform Engineer IV implements and maintains cloud-native AWS data platforms for scientific and environmental workloads. You will build the pipelines, storage, integrations, APIs, automation, governance, and monitoring that move scientific, geospatial, and Earth observation data from source to the researchers and programs that rely on it, working from the Senior Architect's designs while writing clean, tested code and providing feedback on feasibility and trade-offs.

Responsibilities include:

• Build cloud-native data platforms on AWS that support scientific and environmental data workloads
• Build and maintain ingestion and processing pipelines for large, complex scientific datasets, including Earth observation, weather, and environmental data
• Implement storage layers designed for scientific data formats and access patterns (e.g., NetCDF, HDF5, Zarr, GeoTIFF)
• Implement data governance, metadata management, and lifecycle controls so federal and scientific data is trusted, discoverable, and well managed, with attention to provenance and reproducibility
• Develop integrations with internal systems, external scientific data sources, and third-party services
• Build APIs and services that expose data and platform capabilities to scientists, analysts, and downstream systems
• Work with scientists and data users to understand how data is produced and consumed, and make sure the platform serves their needs
• Implement cloud-based data platform components from solution designs and direction provided by the Senior Architect
• Automate deployment, orchestration, testing, and operations using infrastructure as code and CI/CD practices
• Add monitoring, logging, and alerting so pipelines are observable and reliable
• Give the Senior Architect feedback on feasibility and trade-offs during implementation
• Write clean, tested, well-documented code and participate in code reviews
• Perform other duties and responsibilities as assigned

What You Will Bring

Basic Qualifications

• Bachelor's degree in Computer Science, Engineering, a physical, environmental, or Earth science, or a related field, or equivalent practical experience
• 8+ years of professional software or data engineering experience, including building production data platforms
• Demonstrated experience building pipelines or platforms for scientific, geospatial, environmental, or Earth observation data
• Hands-on experience with scientific or geospatial data formats (e.g., NetCDF, HDF5, Zarr, GeoTIFF)
• Strong proficiency in Python and SQL
• Hands-on experience implementing solutions in a major cloud environment, with AWS strongly preferred
• Experience building data ingestion and processing pipelines with workflow or orchestration tools
• Experience with cloud data storage technologies (object storage, data lakes, warehouses, databases)
• Experience implementing data governance, cataloging, and lifecycle management practices
• Experience delivering solutions in federal or other regulated environments, including security and compliance requirements (e.g., NIST, FedRAMP)
• Experience building APIs and system integrations
• Experience with infrastructure as code and CI/CD tooling
• Solid software engineering fundamentals: testing, version control (Git), code review, and documentation
• Ability to interpret architectural designs and implement them as scalable, production-ready solutions
• Strong communication skills and the ability to collaborate with architects, scientists, and cross-functional teams

Preferred Qualifications

• Experience supporting Earth observation, weather, climate, or environmental data platforms
• Experience working in scientific research, federal science, or government environments
• Experience with distributed processing frameworks (e.g., Spark, Dask, Flink) and streaming technologies (e.g., Kafka, Kinesis)
• Familiarity with modern data and table formats (e.g., Parquet, Avro, Iceberg, Delta Lake)
• Experience with data governance, metadata management, and data catalog tools
• Experience with monitoring and observability tools (e.g., CloudWatch, Prometheus, Grafana, Datadog)
• Background in cloud security, IAM, and cost optimization (FinOps)
• Experience with containerization (Docker)
• AWS certification (e.g., Solutions Architect, Data Engineer, or DevOps Engineer)