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Palantir Developers: What They Do and How to Find the Right Ones
Palantir developers explained — what they build, the skill levels that matter, how to evaluate experience, and the build vs hire decision most organizations face.
Palantir developers are not the same as general software developers who’ve taken a Palantir course.
The distinction matters because the supply of people calling themselves Palantir developers has grown much faster than the supply of people who’ve actually built and deployed production Palantir systems. The former can create confusion. The latter can create real operational value.
Understanding what Palantir developers actually do — and what separates experienced ones from people who’ve watched the tutorials — is the starting point for finding the right people.
What Palantir Developers Actually Build
Palantir Foundry is a platform, not a product. What gets built on it depends entirely on the organization’s needs. Palantir developers work across several distinct areas:
Ontology Development
The ontology is the conceptual foundation of every Palantir Foundry implementation. It defines the objects that matter to the business — people, assets, transactions, events, locations — and the relationships between them. It maps raw source data into these structured objects through transformation pipelines.
Ontology development requires both technical skill (understanding data modeling, pipeline construction, and Palantir’s object type system) and business domain knowledge (understanding what the organization’s data actually represents and how it’s used operationally).
Getting the ontology right is the most consequential technical decision in a Palantir implementation. Ontology design mistakes are expensive to fix because everything built on top of the ontology has to be revised when the foundation changes.
Pipeline Development
Data doesn’t arrive in Foundry ready to use. It comes from source systems — ERP systems, CRMs, operational databases, external data providers — in formats that need to be cleaned, transformed, and mapped into the ontology.
Pipeline development in Palantir uses:
- Code Repositories for Python and Spark-based transformations
- Pipeline Builder for visual data pipeline construction
- Fusion for no-code data preparation
Experienced Palantir pipeline developers understand not just the tools but the data engineering principles behind effective pipeline design — incremental processing, schema evolution, data quality validation, lineage documentation.
Application Development
Applications in Palantir Foundry are built primarily using Workshop (the main application builder), Quiver (for analytical workspaces), and Object Explorer (for entity-centric views). For more complex applications, TypeScript-based custom extensions are used.
Palantir application developers build the operational interfaces that bring the data platform’s value to end users: dashboards for operational visibility, workflow tools that embed data into decision processes, alert systems, reporting interfaces.
The skill here is understanding both the platform’s application development capabilities and the UX principles that make data tools useful rather than impressive. An application that’s technically well-built but confusing to use defeats its purpose.
AIP Development
Palantir’s AIP (Artificial Intelligence Platform) is the layer that integrates LLM capabilities into Foundry applications. AIP Logic allows developers to build AI-powered workflows — natural language interfaces to data, automated analysis, AI-assisted decision support.
AIP development requires understanding how to design effective LLM interactions within the context of an operational data platform — which is different from general prompt engineering. The data is already structured in the ontology. The question is how to surface it through natural language interactions that are accurate, useful, and safe for operational use.
ML Model Development and Deployment
Palantir has dedicated infrastructure for training and deploying ML models — Modeling Objectives for model development, Model Deployment for inference infrastructure, and native integration with the ontology for feature engineering and model outputs.
Palantir ML developers work at the intersection of data science and platform engineering. They understand both how to build effective models and how to integrate model outputs into the operational workflows where they add value.
The Palantir Developer Skill Hierarchy
| Level | What They Can Do | How to Identify |
| Foundry Analyst | Use pre-built applications, run queries, basic Workshop | Palantir Foundry Analytics badge |
| Foundry Developer | Build pipelines, create applications, ontology development | Palantir Foundry Builder badge, project examples |
| Senior Foundry Developer | Complex ontology design, advanced pipeline architecture, AIP development | Production deployment experience, complex use cases |
| Palantir Architect | End-to-end implementation design, multi-workstream coordination, governance | Multiple production implementations, cross-domain experience |
The certification badges are a starting point, not a destination. Someone with a Builder certification who’s worked on one implementation for six months is very different from someone with the same certification who’s led ten implementations across multiple industries.
What to Look for When Evaluating Palantir Developers
Production Experience, Not Demo Experience
The most important question to ask any Palantir developer: “Tell me about a Palantir system you’ve built that’s currently in production — what does it do, what was the hardest part of building it, and what would you do differently?”
The specificity of the answer tells you everything. Vague answers about “building dashboards” or “implementing data pipelines” without specific technical detail mean the experience is superficial. Specific answers about ontology design decisions, pipeline architecture choices, performance optimization challenges, and lessons learned in production mean the experience is real.
Ontology Design Experience
Strong Palantir developers can talk specifically about ontology design — how they’ve modeled complex business domains, how they’ve handled schema evolution as requirements changed, how they’ve resolved conflicts between different teams’ views of the same entities.
Ask a developer to walk you through an ontology they’ve designed. A good answer involves the business problem they were solving, the modeling decisions they made and why, the tradeoffs they navigated, and how the ontology supported the applications built on top of it.
Domain Knowledge Alignment
Palantir is deployed across sectors with very different data environments: defense and intelligence, financial services, healthcare, manufacturing, energy, government. A developer with deep experience in defense implementations may have limited familiarity with the data structures and regulatory requirements of financial services.
For industry-specific implementations, domain knowledge matters alongside platform knowledge. Someone who understands both the platform and the domain will build better solutions faster than someone who understands only the platform.
Integration Experience
Most Palantir implementations connect to complex existing data environments — legacy ERP systems, proprietary operational databases, external data providers, real-time data streams. Integration experience — knowing how to build robust, maintainable pipelines from complex source systems — is a distinct and valuable skill.
Working With Palantir Developers: What to Expect
| Engagement Type | What the Developer Does | What You Provide |
| Ontology design | Models business domain, designs object type hierarchy | Business requirements, source data access |
| Pipeline development | Builds data transformation and loading pipelines | Source system access, data documentation |
| Application development | Builds operational tools and dashboards | User requirements, workflow documentation |
| AIP development | Builds LLM-powered application features | Use case definition, access to relevant data |
| ML development | Trains and deploys predictive models | Labeled data, feature definitions, success criteria |
| Performance optimization | Improves pipeline and application performance | Access to current implementation, performance metrics |
The most important thing you provide, regardless of engagement type, is access — to source systems, to domain experts who understand the business context, and to end users who can give feedback on what’s being built.
Palantir development done in isolation from the business context it’s supposed to serve produces technically correct systems that don’t solve the actual problem.
The Build vs. Hire Question
Organizations using Palantir face a choice between building internal Palantir developer capability and engaging external Palantir developers for specific work.
| Approach | Pros | Cons | Best For |
| Build internal team | Deep organizational knowledge, long-term capability | Slow to build, expensive, talent is scarce | Organizations with large, ongoing Palantir investments |
| External developers | Faster time to value, experienced from day one | Knowledge transfer required, dependency on external | Initial implementation, specific projects, capability gaps |
| Hybrid | External leads initial build, internal takes over | Requires good knowledge transfer planning | Most organizations |
The hybrid approach — using experienced external Palantir developers to lead the initial implementation while building internal capability in parallel — works well for most organizations. It gets the implementation done correctly while avoiding permanent dependency on external resources.
Palantir developers who can deliver production value are a specific and relatively scarce resource. The platform is powerful and the developer community is growing, but genuine production experience remains concentrated in a relatively small group of practitioners.
Finding the right ones requires looking past certifications to actual production experience — what they’ve built, how it performed, what they learned. That’s the filter that separates Palantir developers who can help you from ones who’ll learn on your project.
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About the Author: Penelope Klein
