Data Engineer, Senior

Booz Allen Hamilton•Nellis Air Force Base, NV

About The Position

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 an experienced data engineer 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 help build advanced technology solutions and implement data engineering activities on some of the most mission-driven projects in the industry. You’ll deploy and develop pipelines and platforms that organize and make disparate data meaningful. Here, you’ll work with and guide a multi-disciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment. You’ll use your experience in analytical exploration and data examination while you manage the assessment, design, building, and maintenance of scalable platforms for your clients. Work with us to use data for good. Join us. The world can’t wait.

Requirements

6+ years of experience architecting and delivering AI enabled mission or enterprise systems, including data integration, secure compute environments, and operator facing applications in restricted or classified settings
6+ years of experience designing and implementing data pipelines, ontologies, and application workflows on platforms such as Palantir Foundry or Gotham, including airgapped, degraded connectivity, or multiclassification environments
6+ years of experience developing secure analytics ecosystems, including log pipelines, monitoring frameworks, operational dashboards, or intelligence fusion platforms in greenfield or modernization efforts
6+ years of experience implementing DevSecOps practices, including containerization, orchestration, CI/CD, and automated testing within constrained or limited toolchain environments
6+ years of experience integrating AI/ML capabilities into production workflows or operator tooling, such as LLM enabled assistants, model augmented decision aids, or automation solutions
6+ years of experience making architectural tradeoffs under constrained mission conditions, including limited infrastructure, fragmented networks, evolving requirements, or high pressure timelines
Experience rapidly learning unfamiliar technologies, and supporting engineering teams through implementation
Experience communicating architectural decisions and solution impacts to engineers, government clients, and senior nontechnical stakeholders
Secret clearance
Bachelor’s degree

Nice To Haves

Experience supporting multi cloud, multi enclave, or multiclassification architectures, including environments spanning NIPR, SIPR, JWICS, or coalition networks
Experience contributing to defense programs or large systems integration efforts involving acquisition, compliance, or organizational complexity
Experience supporting DoD installations, operational units, or C2 systems in environments with incomplete documentation or inconsistent technical baselines
Experience developing agentic workflows, automation pipelines, or custom tooling to increase engineering throughput or reduce operator workload
Experience serving in a technical lead capacity responsible for system design and delivery
Experience participating in tiger teams, flyaway teams, or rapid response missions requiring accelerated delivery under complex conditions
Top Secret clearance
AWS, Palantir Foundry or Gotham, Kubernetes, or comparable architecture frameworks certifications

Responsibilities

Deploy and develop pipelines and platforms that organize and make disparate data meaningful.
Work with and guide a multi-disciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment.
Manage the assessment, design, building, and maintenance of scalable platforms for clients.
Architecting and delivering AI enabled mission or enterprise systems, including data integration, secure compute environments, and operator facing applications in restricted or classified settings.
Designing and implementing data pipelines, ontologies, and application workflows on platforms such as Palantir Foundry or Gotham, including airgapped, degraded connectivity, or multiclassification environments.
Developing secure analytics ecosystems, including log pipelines, monitoring frameworks, operational dashboards, or intelligence fusion platforms in greenfield or modernization efforts.
Implementing DevSecOps practices, including containerization, orchestration, CI/CD, and automated testing within constrained or limited toolchain environments.
Integrating AI/ML capabilities into production workflows or operator tooling, such as LLM enabled assistants, model augmented decision aids, or automation solutions.
Making architectural tradeoffs under constrained mission conditions, including limited infrastructure, fragmented networks, evolving requirements, or high pressure timelines.
Rapidly learning unfamiliar technologies, and supporting engineering teams through implementation.
Communicating architectural decisions and solution impacts to engineers, government clients, and senior nontechnical stakeholders.