Data Engineer

• 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.

• One of the following combinations of education certification and recent specialized experience:
  • Bachelors degree plus 3 years of recent specialized experience; or
  • Associates 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.

• 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.

Expression offers competitive salaries and benefits such as:

• 401k matching

• PPO and HDHP medical/dental/vision insurance

• Education reimbursement

• Complimentary life insurance

• Generous PTO and holiday leave

• Onsite office gym access

• Commuter Benefits Plan