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.
Clearance: Secret clearance required ability to obtain TS/SCI clearance
Location: Onsite Washington DC
Key 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.
Required Qualifications- One of the following combinations of education, certification, and recent specialized experience:
- 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.
Preferred Qualifications- 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.
Benefits:Expression offers competitive salaries and benefits, such as:
- 401k matching
- PPO and HDHP medical/dental/vision insurance
- Education reimbursement up to $10,000/yr
- Complimentary life insurance
- Generous PTO and 11 days of holiday leave
- Onsite gym facility and trainer
- Commuter Benefits Plan
- In-office Cold Brew Coffee
