Meteorological Data Management Engineer at Booz Allen in McLean, VA

Meteorological Data Management Engineer Location: McLean, VA Meteorological Data Management Engineer The Opportunity: Ever-expanding technology like IoT, machine learning, and artificial intelligence means that there’s more structured and unstructured data available today than ever before. As a data engineer, you know that organizing data can yield pivotal insights when it’s gathered from disparate sources. We need a data professional like you to help our clients find answers in their data to impact important missions from fraud detection to cancer research, to national intelligence. As a data engineer at Booz Allen, you’ll use your skills and experience to help build advanced technology solutions and implement data engineering activities on some of the most mission-driven projects in the industry. You’ll develop and deploy the pipelines and platforms that organize and make disparate data meaningful. Here, you’ll work with a multi-disciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment. You’ll sharpen your skills in analytical exploration and data examination while you support the assessment, design, development, and maintenance of scalable platforms for your clients. Due to the nature of work performed within this facility, U.S. citizenship is required. Work with us to use data for good. Join us. The world can’t wait. You Have: 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 Nice If You Have: 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 equivalentmetadata 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 Vetting: Applicants selected will be subject to a government investigation and may need to meet eligibility requirements of the U.S. government client. Compensation At Booz Allen, we celebrate your contributions, provide you with opportunities and choices, and support your total well-being. Our offerings include health, life, disability, financial, and retirement benefits, as well as paid leave, professional development, tuition assistance, work-life programs, and dependent care. Our recognition awards program acknowledges employees for exceptional performance and superior demonstration of our values. Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in Booz Allen’s benefit programs. Individuals that do not meet the threshold are only eligible for select offerings, not inclusive of health benefits. We encourage you to learn more about our total benefits by visiting the Resource page on our Careers site and reviewing Our Employee Benefits page. Salary at Booz Allen is determined by various factors, including but not limited to location, the individual’s particular combination of education, knowledge, skills, competencies, and experience, as well as contract-specific affordability and organizational requirements. The projected compensation range for this position is $62,000.00 to $141,000.00 (annualized USD). The estimate displayed represents the typical salary range for this position and is just one component of Booz Allen’s total compensation package for employees. This posting will close within 90 days from the Posting Date. Identity Statement As part of the hiring process, we will ask you to complete an identity verification process that leverages advanced biometrics and artificial intelligence to ensure authenticity and protect against identity fraud. You are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud. Candidate AI Usage Policy AI is a part of our daily work at Booz Allen, and we are committed to the responsible and ethical use of AI tools. However, we want to ensure a fair candidate process based on your own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with responses during interviews (whether in-person or virtual) is prohibited unless permission is explicitly provided. Work Model Our people-first culture prioritizes the benefits of collaboration, whether it occurs in person or virtually. To support engagement and effective communication, employees working virtually are generally expected to have their cameras on during meetings. Remote: If this position is listed as remote, there may still be occasions when you are required to work in person at a Booz Allen or customer facility. Hybrid: If this position is listed as hybrid, you will be expected to work from a Booz Allen facility frequently, in alignment with leadership expectations and the needs of the role. You may also be required to work from or visit a customer facility. Onsite: If this position is listed as onsite, work will primarily be performed at a Booz Allen office or customer facility, where employees will collaborate directly with colleagues and customers as required by the role. Commitment to Non-Discrimination All qualified applicants will receive consideration for employment without regard to disability, status as a protected veteran or any other status protected by applicable federal, state, local, or international law.