Forward Deployed Engineer - Palantir

Nextgen Invent Corporation•New York, NY

About The Position

NextGen Invent is seeking a highly motivated Forward Deployed Engineer with hands-on experience in Palantir Foundry and its Artificial Intelligence Platform (AIP). The role works directly with clients to address complex business challenges through data, analytics, and AI-driven solutions. It combines data engineering, software engineering, solution architecture, and client consulting. The engineer partners with business and technical stakeholders to understand requirements, design scalable data solutions, and deliver applications using Palantir Foundry and AIP. The role involves working in client-facing environments, translating business needs into technical solutions, and driving projects from concept through deployment. The position is based in New York/New Jersey, with stated work timings of 8:00 AM to 5:00 PM EST.

Requirements

Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related technical discipline.
2–6 years of experience in Data Engineering, Software Engineering, Analytics Engineering, or a related technical role.
At least 1 year of hands-on Palantir Foundry experience in production environments.
Experience with Palantir AIP and AI-enabled application development.
Strong expertise in Foundry Pipelines, Ontology Modeling, data integration, data pipeline development, complex data transformations, and Foundry applications.
Experience integrating and managing data from multiple enterprise systems and external data sources.
Strong SQL skills for data analysis, transformation, and optimization.
Hands-on programming experience in Python.
Experience with PySpark, Spark SQL, or distributed data processing frameworks.
Understanding of APIs, data modeling, ETL/ELT processes, and modern data architecture principles.
Strong analytical, troubleshooting, and problem-solving abilities.
Strong stakeholder management and client-facing communication experience.

Nice To Haves

Experience implementing enterprise-scale Palantir Foundry solutions.
Experience with Generative AI, LLM integrations, AI agents, and intelligent workflow automation.
Exposure to AWS or Azure cloud platforms.
Experience with Snowflake, Databricks, or modern cloud data platforms.
Knowledge of data governance, security, and data management best practices.
Consulting, implementation, or customer-facing project experience.
Experience supporting Fortune 500 clients or large enterprise environments.
Exposure to manufacturing, healthcare, or life sciences.
Palantir Foundry Certification.
Palantir Data Engineer Certification.
Palantir AIP Certification.
AWS or Azure Certified Cloud Practitioner, or AWS/Azure Solutions Architect certification.
Databricks Data Engineer Associate certification.

Responsibilities

Partner with client stakeholders to understand business processes, challenges, and strategic objectives.
Conduct discovery sessions, solution workshops, and technical discussions with business and technology teams.
Translate business requirements into scalable, maintainable technical solutions within AI Foundry.
Present solution architectures, implementation approaches, and technical recommendations to client teams and leadership.
Design, develop, and maintain data pipelines in Palantir Foundry.
Build and manage Ontology models, business objects, and relationships for enterprise use cases.
Integrate data from enterprise applications, databases, APIs, and external systems into Foundry.
Develop complex transformation logic and optimize workflows for performance, scalability, and reliability.
Configure and enhance Foundry applications, operational workflows, and business-facing use cases.
Design and implement AI-enabled solutions using Palantir AIP.
Build and support AI-driven workflows, operational applications, and decision-support systems.
Identify opportunities with stakeholders to incorporate Generative AI and LLMs into business processes.
Develop and optimize AIP use cases aligned with business objectives and operational efficiency goals.
Develop scalable data solutions using SQL, Python, PySpark, and modern data engineering practices.
Design and implement ETL/ELT processes and data integration frameworks.
Ensure data quality, governance, security, and lineage across enterprise data ecosystems.
Optimize pipeline performance and support high-volume enterprise data processing.
Deliver end-to-end solutions from requirements gathering through deployment, testing, and post-production support.
Troubleshoot production issues, perform root-cause analysis, and implement sustainable fixes.
Collaborate with data engineers, software engineers, architects, product managers, and client stakeholders.
Create and maintain technical documentation, architecture diagrams, operational procedures, and user guides.
Contribute reusable frameworks, accelerators, and best practices across projects.