Data Engineer - CDAO

Agile Defense•Washington, DC

30d•$145,000 - $165,000•Onsite

About The Position

Agile Defense is seeking a Data Scientist / Engineer to support the design, development, and operational deployment of scalable, AI-enabled data solutions within the Department of Defense’s CDAO ADA IR program. This role is part of a multidisciplinary team integrating advanced analytics, machine learning, and engineering practices into mission-critical environments at Combatant Commands. You will help shape and deploy data pipelines, pre-processing workflows, feature engineering strategies, and machine learning services within secure, containerized environments. The ideal candidate brings a hybrid of statistical modeling fluency and hands-on software engineering expertise. You will collaborate closely with product managers, full-stack developers, platform engineers, and mission stakeholders to transform raw data into meaningful insights and decision-support tools. This role requires strong technical communication skills, a collaborative mindset, and experience working in agile environments that value reproducibility, testing, and continuous delivery. Familiarity with cloud-based data platforms such as Databricks, Palantir, or AWS-native data services is highly preferred.

Requirements

Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field (or equivalent relevant work experience).
3+ years of experience in data engineering or software engineering.
Demonstrated experience in designing and managing complex data pipelines.
Experience with data visualization and storytelling using tools such as Palantir's MSS Workshop and Slate applications.
Must have Top Secret/ SCI clearance to start.
Must be able to work onsite at a SCIF.

Nice To Haves

Master's degree preferred.
4+ years of experience in applied data science, Palantir Foundry development, or data pipeline development.
Proficient in Python, SQL, and distributed data frameworks (e.g., Spark, Databricks, PySpark).
Experience developing ML models from training to deployment using industry-standard tools and libraries (e.g., scikit-learn, TensorFlow, XGBoost).
Familiarity with MLOps, API development, and secure cloud-based environments (e.g., AWS, Azure, Palantir Foundry).
Strong understanding of data validation, model testing, and performance evaluation techniques.
Excellent technical communication skills, with the ability to explain complex concepts to non-technical audiences.
Familiarity with cloud-based data platforms such as Databricks, Palantir, or AWS-native data services.

Responsibilities

Support the design, development, and operational deployment of scalable, AI-enabled data solutions.
Integrate advanced analytics, machine learning, and engineering practices into mission-critical environments.
Shape and deploy data pipelines, pre-processing workflows, feature engineering strategies, and machine learning services within secure, containerized environments.
Collaborate with product managers, full-stack developers, platform engineers, and mission stakeholders.
Transform raw data into meaningful insights and decision-support tools.
Work in agile environments that value reproducibility, testing, and continuous delivery.