Mid Data Engineer, GenAI & AI-Driven Pipelines

Booz Allen Hamilton; Washington (District of Columbia); Hybrid; USD 78,000 - 176,000; Full time

Job summary

Booz Allen Hamilton is seeking a Mid Data Engineer to design, build, and optimize data pipelines and platforms that turn raw data into actionable insights for mission-critical projects. You will collaborate with analysts, developers, and data consumers in an agile setting to deliver scalable data solutions.

You’ll work on data preparation for AI/GenAI models, integrate diverse data sources, and contribute to efforts across fraud detection, research, and national security domains.

Qualifications

• 2+ years of experience with major data science languages such as Python and R
• 2+ years of experience retrieving, merging, and analyzing structured and unstructured data sources from APIs and databases through Python, R, or SQL
• Experience with Large Language Models (LLMs) and Generative AI (GenAI)
• Experience preparing data for use with AI or GenAI models
• Experience with data analytics, data mining, statistical analysis, or AI and ML
• TS/SCI clearance with a polygraph
• Bachelor’s degree and 3+ years of experience in data engineering, or 7+ years of experience in data engineering in lieu of a degree

Responsibilities

• Build pipelines and platforms that organize and make disparate data meaningful
• 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 data engineering platforms for clients

Skills

Python

R

SQL

Education

Bachelor’s degree in Data Engineering or related field

Job description

Booz Allen Hamilton is seeking a Mid Data Engineer to design, build, and optimize data pipelines and platforms that turn raw data into actionable insights for mission-critical projects. You will collaborate with analysts, developers, and data consumers in an agile setting to deliver scalable data solutions.

You’ll work on data preparation for AI/GenAI models, integrate diverse data sources, and contribute to efforts across fraud detection, research, and national security domains.