Data Engineer

Booz Allen Hamilton•Fort Meade, MD

10d•Remote

About The Position

As a data engineer at Booz Allen, you’ll use your expertise to help build advanced technology solutions and lead data engineering activities on some of the most mission-driven projects in the industry. You’ll guide data engineering activities by overseeing the development and deployment of pipelines and platforms that organize and make disparate data meaningful. In this role, you’ll collaborate closely with AI model engineers, network and cloud architects, the ISSE, and other technical leaders to guide the development of scalable, mission‑aligned data pipelines. You’ll apply your expertise in data engineering, exploratory analysis, and feature development to shape how datasets are assessed, designed, transformed, and maintained for AI and ML workloads. While supporting a cross‑functional technical environment, you’ll help ensure high‑quality, well‑governed data flows that enable reliable model performance across operational, research, and enterprise systems. Work with us to use data for good.

Requirements

6+ years of experience as a data engineer
Experience validating or normalizing datasets and developing schemas
Experience implementing dashboards and metrics for data and pipeline monitoring
Experience building automated ETL workflows and pipelines for data processes
Knowledge of data governance practices
Ability to design, implement, and maintain data pipelines
Ability to monitor and improve pipeline performance in coordination with operations teams
TS/SCI clearance with a polygraph
HS diploma or GED and 6+ years of experience with data normalization and data pipelines, or Bachelor’s degree and 4+ years of experience with data normalization and data pipelines

Nice To Haves

10+ years of experience as a data engineer
Experience with cloud environments such as AWS or Azure
Experience supporting data cleaning or orchestration for AI or ML solutions
Bachelor’s degree

Responsibilities

Oversee the development and deployment of pipelines and platforms that organize and make disparate data meaningful.
Guide the development of scalable, mission‑aligned data pipelines.
Apply expertise in data engineering, exploratory analysis, and feature development to shape how datasets are assessed, designed, transformed, and maintained for AI and ML workloads.
Ensure high‑quality, well‑governed data flows that enable reliable model performance across operational, research, and enterprise systems.
Validate or normalize datasets and develop schemas.
Implement dashboards and metrics for data and pipeline monitoring.
Build automated ETL workflows and pipelines for data processes.
Design, implement, and maintain data pipelines.
Monitor and improve pipeline performance in coordination with operations teams.