Palantir Foundry Use Case Engineer

• Configure, build, and operationalize business use cases on the Palantir Foundry platform.

• Focus on:

• Rapid use-case configuration
• Ontology modeling
• Data pipeline development
• Business-facing analytics
• Foundry-native application development
• Work within a Palantir Center of Excellence (CoE) delivery model.
• Deliver solutions through agile, business-led 2-week sprint cycles.
• Translate business problems into Palantir-native solutions.
• Prioritize rapid configuration, structured delivery, and governance rather than heavy custom coding.

Mandatory Technical Skills

• Hands-on experience configuring Palantir Foundry use cases., Use Case Configuration & Delivery
• Configure end-to-end Palantir Foundry use cases.

• Implement:

• Data ingestion
• Ontology setup
• Data transformations
• Analytics dashboards

• Rapidly deliver business-facing applications using:

• Datasets
• Pipelines
• Ontology Manager
• Contour
• Workshop
• Slate
• Work in 2-week Agile sprints.
• Participate in frequent business demos.
• Incorporate business feedback into iterative development.
• Enable rapid business value through configuration rather than extensive custom development.

Ontology & Data Modeling

• Design and configure ontology-driven data models.

• Model business entities and processes such as:

• Quality
• Supply Chain
• Sales
• Manufacturing
• Customer Analytics

• Define and maintain:

• Objects
• Relationships
• Actions
• Business metrics
• Ensure consistency of object definitions and relationships.
• Follow governance standards established by the CoE.

Data Pipelines & Transformations

• Design and develop end-to-end data pipelines using Palantir Foundry.

• Use:

• Code Repositories
• Pipeline Builder
• Transforms
• Workshop
• Contour
• Build and maintain curated datasets.
• Establish data lineage.

• Implement data quality controls across:

• Ingestion
• Transformation
• Serving layers

• Develop transformations using:

• Python
• PySpark
• SQL

• Optimize pipelines for:

• Performance
• Reliability
• Scalability
• Cost efficiency
• Support large-scale datasets.
• Implement batch and near-real-time data processing patterns.
• Integrate multiple enterprise data sources through Foundry data connections and ingestion frameworks.

Business Enablement & Adoption

• Partner with:

• Product Owners
• Business Analysts
• Business Stakeholders
• Translate business requirements into configured Foundry solutions.
• Support self-service analytics.
• Create documentation and walkthroughs for configured use cases.
• Monitor application usage and adoption.

• Use usage insights to guide:

• Enhancements
• Prioritization
• Continuous improvement

Data Governance & Security

• Implement data governance practices within Foundry.

• Configure and manage:

• Access controls
• Data policies
• Platform standards
• Auditing
• Ensure security and compliance requirements are met.
• Maintain data quality and standardization.
• Support data lineage and governance across enterprise datasets.

Integration & Interoperability

• Integrate Palantir Foundry with enterprise systems.

• Work with:

• APIs
• Connectors
• Data connections
• Enterprise ingestion frameworks
• Support data interoperability patterns.
• Enable reliable data exchange between Foundry and enterprise platforms.

CI/CD & Development Practices

• Establish CI/CD practices for Foundry Code Repositories.
• Perform peer code reviews.
• Enforce coding standards.
• Maintain reusable and maintainable Foundry code.
• Support automated deployment practices where applicable.

Production Support

• Troubleshoot production issues.
• Perform root-cause analysis.
• Resolve data and pipeline issues.
• Improve application and pipeline reliability.
• Optimize performance and cost.
• Drive continuous improvement initiatives.

Required Technical Skills

• Palantir Foundry
• Palantir Core
• Foundry Transforms
• Pipeline Builder
• Foundry Code Repositories
• Foundry Ontology
• Ontology Manager
• Contour
• Workshop
• Slate
• Python
• PySpark
• SQL
• AWS
• Data Modeling
• Data Warehousing
• Dimensional Modeling
• Data Quality
• Data Lineage
• Data Governance
• Access Controls
• APIs
• Data Connectors
• CI/CD
• Agile
• Batch Processing
• Near-Real-Time Data Processing

Data Engineering Skills

• End-to-end data pipeline development.
• Curated dataset development.
• Data ingestion.
• Data transformation.
• Data quality validation.
• Data lineage.
• Data warehousing.
• Dimensional modeling.
• Large-scale data processing.
• Batch processing.
• Near-real-time processing.
• Pipeline performance optimization.
• Complex SQL development.
• Complex joins.
• Window functions.
• SQL optimization.

• Datasets
• Pipelines
• Ontology
• Contour
• Workshop
• Slate
• Strong experience with ontology-based data modeling for enterprise analytics.
• Experience with cloud-native environments, particularly AWS.
• Experience with distributed data systems.

• Experience working in:

• Agile environments
• POD-based delivery models
• Short sprint cycles
• Business demo and feedback cycles
• Strong Python skills.
• Strong SQL skills.
• PySpark experience strongly preferred.

Good-to-Have Skills

• Experience building business-facing dashboards and analytics for:

• Quality
• Manufacturing
• Supply Chain
• Customer Analytics

• Understanding of:

• Data governance
• Usage monitoring
• Data standardization
• Center of Excellence (CoE) practices
• Exposure to applied analytics.
• Exposure to pattern detection using Palantir Foundry., Cloud Skills
• AWS
• Cloud-native environments.
• Distributed data systems.
• Cloud-based data processing.
• Cloud data integration.
• Performance and cost optimization.

Agile & Collaboration

• Experience working in Agile/POD-based environments.
• Experience delivering in short sprint cycles.
• Participate in business demos.
• Incorporate frequent stakeholder feedback.
• Strong communication skills.
• Collaborate effectively with technical and business teams.