Data Engineer

Expression•Arlington, VA

2d•Onsite

About The Position

Expression is seeking an experienced Data Engineer to support the design, development, and operational deployment of scalable, AI-enabled data solutions for the Department of Defense CDAO ADA IR program. The Data Engineer will work as part of a multidisciplinary team integrating data engineering, advanced analytics, machine learning, and software engineering capabilities into mission-critical environments supporting Combatant Commands. This role will design and deploy data pipelines, preprocessing workflows, feature-engineering strategies, reusable data services, and machine learning capabilities within secure, containerized environments. The successful candidate will collaborate with product managers, full-stack developers, platform and DevSecOps engineers, data scientists, and mission stakeholders to transform structured and unstructured data into operational insights and decision-support capabilities. The role combines data engineering, applied data science, and production ML responsibilities and emphasizes reproducibility, testing, secure deployment, technical communication, and continuous delivery.

Requirements

Bachelor’s degree plus 3 years of recent specialized experience; or Associate’s degree plus 7 years of recent specialized experience; or Major certification plus 7 years of recent specialized experience; or 11 years of recent specialized experience.
Experience with data visualization and data storytelling using tools such as Palantir MSS Workshop and Slate applications.
Proficiency with Python, SQL, and distributed data frameworks, including technologies such as Spark, Databricks, and PySpark.
Experience developing machine learning models from training through deployment using industry-standard tools and libraries such as scikit-learn, TensorFlow, and XGBoost.
Strong technical communication skills with the ability to explain complex concepts to non-technical audiences.
Secret clearance required ability to obtain TS/SCI clearance

Nice To Haves

4+ years of experience in applied data science, Palantir Foundry development, or data-pipeline development.
Familiarity with MLOps, API development, and secure cloud-based environments, including AWS, Azure, or Palantir Foundry.
Strong understanding of data validation, model testing, and performance-evaluation techniques.

Responsibilities

Design, develop, and maintain reusable services for data ingestion, transformation, preprocessing, and feature engineering supporting AI/ML workflows.
Build scalable data pipelines and workflows supporting structured and unstructured mission data.
Implement data science capabilities such as entity resolution, classification, clustering, prediction, anomaly detection, pattern recognition, and decision-support functions.
Develop services within secure, containerized environments using established CI/CD, version-control, testing, and documentation standards.
Collaborate with DevSecOps engineers to integrate data and ML services into secure production environments using technologies such as Databricks, Docker, and Terraform.
Ensure production services meet applicable performance, reliability, security, and architectural requirements for DoD enterprise and cloud-native environments.
Develop and deploy standalone and embedded machine learning models supporting mission decision-making, automation, anomaly detection, and pattern recognition.
Select and implement appropriate modeling approaches using Python, Spark, and cloud-native ML frameworks such as SageMaker and MLflow.
Maintain reproducibility and interpretability of model outputs to support mission transparency and audit requirements.
Package model-inference services using documented APIs for integration with end-user applications, operational dashboards, and other mission capabilities.
Conduct exploratory data analysis to identify patterns, trends, data gaps, and opportunities across structured and unstructured datasets.
Develop data visualizations, analytical outputs, and interpretive summaries supporting stakeholder understanding and product-team decisions.
Translate analytical findings into actionable recommendations using visual, narrative, and quantitative communication methods.
Develop and contribute reusable analysis templates, queries, and analytical workflows to improve delivery efficiency.
Engage product managers and mission users to define data, analytical, and model requirements aligned with operational objectives.
Collaborate with software, platform, and DevSecOps engineers to ensure data science components align with technical constraints, architecture, and deployment patterns.
Participate in Agile sprint planning, retrospectives, demonstrations, and related delivery activities.
Maintain documentation supporting technical accountability, reproducibility, operational handoff, and sustainment.