Data Engineer

Booz Allen Hamilton•Fayetteville, NC

2d•$77,500 - $176,000

About The Position

As a senior Data Engineer at Booz Allen, you will design and build data platforms and pipelines that power mission-critical outcomes. You will work on complex, high-impact programs that help clients solve real-world challenges with data. You’ll collaborate with analysts, AI engineers, developers, and stakeholders in a fast-paced, agile environment. In this role, you will lead technical efforts across assessment, design, development, and sustainment of scalable data platforms while mentoring teammates and driving best practices.

Requirements

Experience as a data engineer supporting large-scale enterprise systems
Experience moving data from on-prem environments to the cloud and automating development workflows, including building, deploying, or managing applications
Experience with CI/CD practices
Experience with SQL, including stored procedures and data modeling
Experience with data governance and data tagging
Ability to work independently and manage tasks with minimal supervision
TS/SCI clearance
HS diploma or GED

Nice To Haves

Experience with Pipeline Builder, AIP, and Foundry's application development ecosystem
Experience with ETL tools such as dbt and Airflow
Experience with NoSQL and graph databases and DevOps tools and automation practices
Experience designing and building data warehouses
Experience working in multiple SDLC models, including Agile, Waterfall, Iterative, or Spiral
Experience with open table formats such as Apache Iceberg
Experience with infrastructure-as-code, including Terraform or CloudFormation
Knowledge of data lakes, lakehouse architectures, and database performance concepts such as partitioning
Knowledge of Zero Trust and attribute-based access control (ABAC)

Responsibilities

Design, build, and maintain scalable data pipelines and platforms.
Develop secure, efficient Python for data processing, automation, and warehousing.
Migrate and integrate data from on-premises systems to cloud environments.
Implement CI/CD pipelines to automate build, test, and deployment workflows.
Develop SQL scripts and stored procedures for data processing and transformation.
Apply data modeling best practices.
Support data governance, metadata tagging, and access controls.
Partner with cross-functional teams to deliver end-to-end data solutions.
Provide technical guidance in a complex enterprise environment.