Platform Engineer

Overview

We are seeking an experienced Palantir Platform Engineer to support the design, development, integration, and optimization of enterprise data pipelines within the Palantir Foundry ecosystem. The ideal candidate will have strong experience with ETL development, Python, PySpark, data ontology, and data governance, along with a solid understanding of DevOps methodologies and modern data engineering practices.

In this role, you will be responsible for integrating custom ETL workflows with Palantir tooling, improving data observability, and ensuring efficient data transformation and validation. You will collaborate with cross-functional teams to develop scalable data solutions, leverage AI-driven technologies to enhance pipeline performance, and deliver high-quality data products that support advanced analytics and mission-critical operations.

What will you do?

• Design, develop, and maintain scalable ETL pipelines within the Palantir Foundry environment.
• Integrate custom ETL conditioners with Palantir tooling to streamline data ingestion, transformation, and processing.
• Develop and optimize data pipelines using Python and PySpark to support large-scale data processing.
• Implement and maintain data ontology models to support structured data relationships, integration, and accessibility.
• Establish logging, monitoring, and observability solutions to provide end-to-end visibility into data transformation workflows.
• Leverage AI and automation technologies to improve pipeline efficiency, accelerate data conditioning, and optimize processing performance.
• Implement data governance standards and best practices to ensure data integrity, consistency, security, and compliance.
• Develop and execute data validation processes to ensure the accuracy, completeness, and reliability of data products.
• Support the integration of validated data products into advanced analytics platforms and data exploitation tools.
• Apply DevOps principles to automate deployments, streamline development workflows, and maintain reliable production environments.
• Identify and troubleshoot pipeline failures, data quality issues, and performance bottlenecks.
• Collaborate with data engineers, software developers, analysts, and stakeholders to translate business and technical requirements into scalable data solutions.
• Develop technical documentation covering data pipelines, ontology models, system integrations, and operational procedures.
• Continuously evaluate emerging technologies and methodologies to improve data engineering capabilities and platform performance.

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
• Hands-on experience working with Palantir Foundry and its data integration capabilities.
• Strong understanding of Ontology and Data Ontology, including data modeling, relationships, and integration.
• Demonstrated experience designing, developing, and integrating complex ETL pipelines.
• Proficiency in Python and PySpark for data engineering, automation, and large-scale data processing.
• Experience implementing logging, monitoring, and observability solutions for data pipelines and enterprise platforms.
• Knowledge of data governance principles, including data quality, integrity, security, and lifecycle management.
• Familiarity with DevOps methodologies, CI/CD pipelines, automation, and deployment processes.
• Experience leveraging AI or machine learning technologies to enhance and optimize data processing workflows.
• Strong understanding of data validation, transformation, and quality assurance practices.
• Experience troubleshooting complex data integration issues and optimizing pipeline performance.
• Strong analytical, problem-solving, and communication skills.
• Ability to collaborate effectively with technical teams and stakeholders in a fast-paced environment.
• Active TS/SCI W/ Polygraph required.
• Bachelor's degree in Computer Science, Engineering, Finance, or a related technical field, or equivalent practical experience
• Hands-on experience working with Palantir Foundry and its data integration capabilities.
• Strong understanding of Ontology and Data Ontology, including data modeling, relationships, and integration.
• Demonstrated experience designing, developing, and integrating complex ETL pipelines.
• Proficiency in Python and PySpark for data engineering, automation, and large-scale data processing.
• Experience implementing logging, monitoring, and observability solutions for data pipelines and enterprise platforms.
• Knowledge of data governance principles, including data quality, integrity, security, and lifecycle management.
• Familiarity with DevOps methodologies, CI/CD pipelines, automation, and deployment processes.
• Experience leveraging AI or machine learning technologies to enhance and optimize data processing workflows.
• Strong understanding of data validation, transformation, and quality assurance practices.
• Experience troubleshooting complex data integration issues and optimizing pipeline performance.
• Strong analytical, problem-solving, and communication skills.
• Ability to collaborate effectively with technical teams and stakeholders in a fast-paced environment.
• Design, develop, and maintain scalable ETL pipelines within the Palantir Foundry environment.
• Integrate custom ETL conditioners with Palantir tooling to streamline data ingestion, transformation, and processing.
• Develop and optimize data pipelines using Python and PySpark to support large-scale data processing.
• Implement and maintain data ontology models to support structured data relationships, integration, and accessibility.
• Establish logging, monitoring, and observability solutions to provide end-to-end visibility into data transformation workflows.
• Leverage AI and automation technologies to improve pipeline efficiency, accelerate data conditioning, and optimize processing performance.
• Implement data governance standards and best practices to ensure data integrity, consistency, security, and compliance.
• Develop and execute data validation processes to ensure the accuracy, completeness, and reliability of data products.
• Support the integration of validated data products into advanced analytics platforms and data exploitation tools.
• Apply DevOps principles to automate deployments, streamline development workflows, and maintain reliable production environments.
• Identify and troubleshoot pipeline failures, data quality issues, and performance bottlenecks.
• Collaborate with data engineers, software developers, analysts, and stakeholders to translate business and technical requirements into scalable data solutions.
• Develop technical documentation covering data pipelines, ontology models, system integrations, and operational procedures.
• Continuously evaluate emerging technologies and methodologies to improve data engineering capabilities and platform performance.