Platform Engineer

Overview

VTG is seeking a Platform Engineer to support the design, integration, and optimization of data pipelines within the Palantir ecosystem. This role will focus on integrating custom ETL workflows with Palantir tooling, improving data observability, accelerating data conditioning, and ensuring stakeholders have end-to-end visibility into data transformation processes.

What will you do?

• Design, develop, and maintain scalable ETL data pipelines within the Palantir ecosystem.
• Connect custom ETL conditioners and workflows to Palantir tooling.
• Develop data transformation and conditioning solutions using PySpark and Python.
• Work with Palantir Foundry and ontology models to organize, integrate, and manage complex datasets.
• Implement logging, monitoring, and observability capabilities across data pipelines.
• Provide stakeholders with end-to-end visibility into data transformation and conditioning processes.
• Leverage AI capabilities to augment data pipelines and improve processing speed and efficiency.
• Apply data governance best practices throughout the data lifecycle.
• Perform validation and quality assurance of data products to ensure accuracy, reliability, and readiness for downstream use.
• Integrate validated data products into advanced analytics and data exploitation tools.
• Support deployment and operational activities using DevOps methodologies and practices.
• Collaborate with cross-functional engineering, data, and stakeholder teams to deliver reliable and actionable data products.
• Identify opportunities to improve data conditioning, pipeline performance, scalability, and observability.

Do you have what it takes?

• Active TS/SCI W/ Polygraph required.
• Bachelor's degree in Computer Science, Engineering, Finance, or a related technical field, or equivalent practical experience
• Experience with Palantir Foundry.
• Strong understanding of Ontology and Data Ontology.
• Demonstrated experience designing, building, and integrating ETL pipelines.
• Proficiency with PySpark and Python.
• Experience implementing logging and monitoring solutions.
• Knowledge of data governance best practices.
• Familiarity with DevOps methodologies.
• Experience leveraging AI to augment or optimize data pipelines.
• Strong experience with data validation and data quality assurance.
• Experience working with large or complex datasets and data transformation workflows.