Consultant (GB)
Data Foundry Engineer
· Design, develop, and maintain end-to-end data pipelines using Palantir Foundry (Pipeline Builder, Code Repositories, Data Lineage, Ontology, Workshop, Quiver, etc.).
· Implement batch, incremental, and streaming ETL/ELT workflows with reusable, production-ready components.
· Build and maintain high-quality, version-controlled data products aligned to business use cases.
· Work with federated data environments, handling diverse sources and complex integration requirements.
· Design, extend, and maintain Foundry ontologies, including object types, link types, property types, and semantic relationships.
· Build semantic layers that support analytics, operational workflows, and machine learning use cases.
· Embed strong data quality principles through validation checks, lineage tracking, and governance frameworks.
· Monitor pipeline performance and optimize for scalability, cost efficiency, and speed.
· Implement automated testing, CI/CD patterns, and deployment best practices for Foundry-based data assets.
· Troubleshoot production issues, ensure root-cause analysis, and drive long-term fixes.
· Translate ambiguous business requirements into scalable data engineering solutions.
· Support Foundry Workshop apps, dashboards, and end-user analytical experiences.
· Communicate clearly with technical and non-technical stakeholders, ensuring shared understanding of data design and pipeline behavior.
· Design, develop, and maintain end-to-end data pipelines using Palantir Foundry (Pipeline Builder, Code Repositories, Data Lineage, Ontology, Workshop, Quiver, etc.).
· Implement batch, incremental, and streaming ETL/ELT workflows with reusable, production-ready components.
· Build and maintain high-quality, version-controlled data products aligned to business use cases.
· Work with federated data environments, handling diverse sources and complex integration requirements.
· Design, extend, and maintain Foundry ontologies, including object types, link types, property types, and semantic relationships.
· Build semantic layers that support analytics, operational workflows, and machine learning use cases.
· Embed strong data quality principles through validation checks, lineage tracking, and governance frameworks.
· Monitor pipeline performance and optimize for scalability, cost efficiency, and speed.
· Implement automated testing, CI/CD patterns, and deployment best practices for Foundry-based data assets.
· Troubleshoot production issues, ensure root-cause analysis, and drive long-term fixes.
· Translate ambiguous business requirements into scalable data engineering solutions.
· Support Foundry Workshop apps, dashboards, and end-user analytical experiences.
· Communicate clearly with technical and non-technical stakeholders, ensuring shared understanding of data design and pipeline behavior.