Data Engineer - NORTHCOM

Job Description

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.

Education and Background

Typically has a Bachelor's degree in Computer Science, Information Systems, Data Engineering, or a related field (masters degree preferred)., and 3+ years of experience in data engineering or software engineering, with demonstrated experience in designing and managing complex data pipelines., or equivalent relevant work experience; e.g., each year of work experience may be substituted for each year of education required.

Years of Experience

3+ years

Required Skills

Experience with data visualization and storytelling using tools such as Palantir's MSS Workshop and Slate applications

Preferred Skills

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.

Working Conditions

Must be able to work onsite at a SCIF