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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Palantir Foundry Use Case Engineer - **Company:** TAL International Container Corporation - **Location:** Ewing Township, NJ, United States - **Experience:** Expert - **Salary:** $98,000.0 - $170,000.0 - **Contract:** Temporary contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Amazon Web Services, Computer Programming, Continuous Integration, Data Governance, Data Transformation, Data Warehousing, Dimensional Modeling, Distributed Data Store, Interoperability, Python (Programming Language), Pattern Recognition, Scrum Methodology, Query Optimization, Standard Sql, SQL Databases, Software Repository, Data Processing, Enterprise Software Applications, Data Ingestion, Sql Optimization, Pyspark, Semi-structured Data, Data Lineage, Stream Processing, Data Pipelines - **Published:** August 29, 2026 - **Apply:** https://www.careerjet.com/job/us864bb94118bb6f496635fdd78c65aad7/eaa ## About the Role * Datasets * Pipelines * Ontology * Contour * Workshop * Slate * Experience with ontology-based data modeling for enterprise analytics. * Exposure to: * AWS cloud environments * Distributed data systems * Experience working in: * Agile environments * POD-based delivery models * Short sprint cycles * Business demo and feedback cycles Good-to-Have Skills * Experience developing 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 * Basic exposure to: * Applied analytics * Pattern detection * Foundry-based analytics use cases, * Palantir Foundry * Transforms * Pipeline Builder * Code Repositories * Curated Datasets * Data Modeling * Foundry Ontology * Workshop * Contour * Programming: * Python - Mandatory * PySpark - Strongly preferred * SQL: * Advanced SQL * Complex joins * Window functions * Query optimization * Data Governance * Access Controls * Data Quality * Data Warehousing * Dimensional Modeling * Batch Data Processing * Near-Real-Time Data Processing * AWS * APIs and Connectors * CI/CD * Agile Generic Managerial Skills * Good communication skills. * Strong stakeholder collaboration. * Ability to work effectively in Agile/POD-based teams. * Ability to communicate technical concepts to business stakeholders. ## Description * Configure, build, and operationalize business use cases on the Palantir Foundry platform. * Focus on: * Use-case configuration * Ontology modeling * Data pipeline development * Business-facing analytics * Work within an agile, business-led 2-week sprint model. * Participate as part of a Palantir Center of Excellence (CoE) delivery model. * Translate business problems into Palantir-native solutions. * Focus on rapid configuration and delivery rather than heavy custom coding. Mandatory Skills * Hands-on experience configuring Palantir Foundry use cases, including, Use Case Configuration & Delivery * Configure end-to-end Palantir Foundry use cases covering: * Data ingestion * Ontology setup * Data transformations * Analytics dashboards * Build business-facing applications using: * Datasets * Pipelines * Ontology Manager * Contour * Workshop * Slate * Work in 2-week Agile sprints. * Participate in frequent business demonstrations and feedback sessions. * Deliver business value through iterative configuration and development. Ontology & Data Modeling * Design and configure ontology-driven data models aligned with business entities and processes. * Model business domains such as: * Quality * Supply Chain * Sales * Manufacturing * Maintain consistency in: * Object definitions * Relationships * Business metrics * Follow ontology governance standards established by the CoE. Data Pipelines & Transformations * Build and maintain Palantir Foundry pipelines. * Develop data transformations using: * Python * PySpark * SQL * Work with structured and semi-structured data. * Integrate data from multiple enterprise sources using Foundry: * Data Connections * Ingestion frameworks * Optimize pipelines for: * Performance * Reliability * Cost efficiency * Support cloud-based deployments, primarily on AWS. Business Enablement & Adoption * Partner with: * Product Owners * Business Analysts * Business Stakeholders * Translate business requirements into configured Foundry solutions. * Support self-service analytics and business enablement. * Create documentation and walkthroughs for configured use cases. * Monitor application usage and adoption. * Use adoption insights to guide enhancements and prioritization. Additional Responsibilities * Design and develop end-to-end data pipelines using Palantir Foundry tools such as: * Code Repositories * Pipeline Builder * Transforms * Workshop * Contour * Build and maintain: * Curated datasets * Data lineage * Data quality controls * Implement data quality across: * Ingestion * Transformation * Serving layers * Implement Foundry Ontology, including: * Objects * Actions * Relationships * Enable operational workflows through ontology-driven applications. * Develop and support Foundry dashboards and applications. * Collaborate with stakeholders to develop data products iteratively. * Implement security, governance, and compliance using: * Foundry access controls * Data policies * Auditing practices * Integrate Foundry with enterprise systems through: * APIs * Connectors * Data interoperability patterns * Establish CI/CD practices for Foundry Code Repositories. * Perform peer code reviews. * Enforce coding and development standards. * Troubleshoot production issues and perform root-cause analysis. * Drive continuous improvements in reliability and performance., * Palantir * Palantir Foundry * Palantir Core * AWS * Python * PySpark * Ontology-Based Data Modeling * Pipeline Builder * Foundry Transforms * Foundry Ontology * Workshop * Contour ## Related Videos - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [The Best X (Twitter) Accounts for Developers](https://www.wearedevelopers.com/magazine/294-the-best-x-twitter-accounts-for-developers) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)