Meteorological Data Management Engineer

Booz Allen Hamilton is a leading technology firm that focuses on leveraging data for impactful missions. They are seeking a Meteorological Data Management Engineer to help build advanced technology solutions and implement data engineering activities, including developing and deploying data pipelines and platforms.

Responsibilities:

• Work with a multi-disciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment
• Sharpen skills in analytical exploration and data examination while supporting the assessment, design, development, and maintenance of scalable platforms for clients

Requirements:

• 5+ years of experience with meteorological data
• 3+ years of experience managing large-scale data ecosystems, including designing storage strategies such as intelligent tiering for cost optimization and performance
• Experience with metadata management tools such as OpenMetadata, including setup, configuration, and integration into data pipelines to ensure discoverability, lineage tracking, and governance
• Experience integrating storage and metadata strategies into Agile, cross-functional development teams to ensure alignment with real-time and batch processing pipelines
• Knowledge of data lifecycle management strategies, including tiering data across hot, warm, and cold storage layers, retention policies, and archival workflows, to support petabyte-scale and continuously-ingested datasets
• Knowledge of cloud-based storage systems and intelligent tiering features such as AWS S3 Intelligent-Tiering, or in Azure or GCP, including APIs and configuration
• Knowledge of data observability advancements to monitor, assess, and optimize the performance of pipelines and data storage systems in distributed cloud environments
• Ability to work with large datasets using programming languages such as Python, to develop and optimize data organization, storage, and transformation workflows
• Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
• Bachelor's degree
• Experience configuring cloud-native tools for policies on intelligent tiering such as automating data movement between storage tiers using Lambda, Step Functions, or equivalent workflows
• Experience working with OpenMetadata integrations into existing tools such as Apache Airflow, Kubernetes, and large-scale orchestration systems, ensuring metadata catalogs automatically synchronize with pipeline operations
• Experience working with containerized environments such as Docker or Kubernetes, and modern orchestration tools such as Airflow or Prefect, to optimize both metadata workflows and storage pipelines
• Experience working with implementing data governance frameworks such as access controls and lineage policies that integrate with OpenMetadata or equivalent metadata tools
• Knowledge of geospatial datasets or scientific data formats such as NetCDF, GRIB, or HDF5, commonly used in weather or satellite data systems, and their implications for storage architecture
• Knowledge of distributed query engines such as Presto, Trino, Hive, or Spark, tuned for performance on a lakehouse or intelligent tiering-enabled data lake architecture
• Knowledge of real-time data streaming tools and integrations such as Kafka or AWS Kinesis, ensuring metadata tracks changes and tiering strategies accommodate time-sensitive ingestion workflows
• Knowledge of Agile engineering practices, including CI/CD pipelines and collaboration with data engineers, AI engineers, and product teams to deliver optimized data ecosystems
• Ability to analyze usage patterns and recommend optimizations for performance, cost, and data accessibility at scale