Platform and Data Engineer

Booz Allen Hamilton•Honolulu, HI

19h•$61,900 - $141,000

About The Position

As a data engineer at Booz Allen, you will build advanced technology solutions and implement data engineering activities for mission-driven projects. You will develop and deploy pipelines and platforms that organize disparate data and make it meaningful, and work with a multidisciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment. You will assess, design, build, and maintain scalable platforms, using analytical exploration and data examination to support client missions such as fraud detection, cancer research, and national intelligence. The role focuses on full-stack data engineering, including data engineering back ends and interactive front ends. You will work closely with clients to understand their questions and needs, examine data-rich environments, and develop analytics systems that turn disparate data into useful answers. You will work alongside teammates in classified environments and use a range of tools and frameworks to advise clients as they make informed decisions. The position emphasizes production-quality code and strong software and data development practices. The DoD work may involve data analytics efforts such as logistics data analysis for the U.S. Navy. Applicants must hold a Secret clearance and a bachelor’s degree in a STEM field. The projected annual compensation range is $61,900 to $141,000. Booz Allen describes a benefits package that includes health, life, disability, financial, and retirement benefits, paid leave, professional development, tuition assistance, work-life programs, and dependent care. Eligibility for benefits depends on employee status and regularly scheduled hours.

Requirements

At least 3 years of experience in software development, data science, or data engineering, based on impactful industry-oriented data science or full-stack development work.
At least 2 years of experience using Python for data and standard libraries such as NumPy, pandas, matplotlib, and PySpark.
At least 1 year of experience building visualization front ends, such as analytics dashboards or interactive GUI applications and web apps.
At least 1 year of experience communicating technical solutions to external customers and explaining them to non-technical audiences, including leadership.
Experience breaking down complex problems into technical solutions based on customer requirements, including independently.
Experience routinely following SDLC best practices.
Ability to be detail-oriented and proactive.
Ability to articulate and anticipate development risks.
Active Secret clearance is required.
Bachelor’s degree in a STEM field such as Computer Science, Data Science, Mathematics, Statistics, or Engineering.

Nice To Haves

Experience with DoD platforms such as War Data Platform, ADVANA, or Palantir Foundry.
Experience with distributed data and computing tools such as Spark, MapReduce, or Hadoop.
Experience with React and Angular.
Experience with TypeScript.
Experience with generative AI techniques, including large language models.
Experience with cloud-based environments such as AWS.
Experience solving optimization problems from an operations research perspective, such as linear, nonlinear, or constraint programming.
Experience writing production-level code for efficiency, speed, and reliability.
Experience working with high-ranking military personnel.
Master’s degree in a STEM field such as Computer Science, Data Science, Mathematics, Statistics, or Engineering.

Responsibilities

Build and implement data engineering solutions for mission-driven client projects.
Develop and deploy pipelines and platforms that organize disparate data and make it meaningful.
Guide and collaborate with analysts, data engineers, developers, and data consumers in an agile environment.
Assess, design, build, and maintain scalable client platforms.
Work with clients to understand their questions and requirements, then examine their data-rich environments to develop solutions.
Own the development of analytics systems in classified environments.
Use data engineering back ends and interactive front ends to build data applications end to end.
Turn disparate data points into meaningful answers that support client decision-making.
Follow production-quality software and data development practices.