> Markdown version of [/jobs/ext/2576958-hired-data-engineer-palantir-foundry](https://www.wearedevelopers.com/jobs/ext/2576958-hired-data-engineer-palantir-foundry). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Hired - Data Engineer (Palantir Foundry) - **Company:** Capgemini - **Location:** Chicago, IL, United States - **Experience:** Expert - **Salary:** $160,000.0 - $220,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Computing Platforms, Microsoft Azure, Data Architecture, Data Cleansing, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Apache Hive, Python (Programming Language), Meta-Data Management, Performance Tuning, Software Engineering, Data Streaming, Software Repository, Snowflake, Generative AI, Data Strategy, Pyspark, Integration Frameworks, Software Version Control, Data Pipelines - **Published:** August 10, 2026 - **Apply:** https://www.juju.com/job/00000000gmgoxh ## About the Role 8+ years of experience in data engineering or related roles. * Strong proficiency in PySpark, Spark SQL, and Python. * Deep hands-on experience with Palantir Foundry, including: * Data Connections * Pipeline Builder * Contour * Code Repositories * Ontology Management * Workshop and Quiver * Solid understanding of data modeling, data governance, and access control. * Proven ability to troubleshoot complex data issues and optimize performance. * Excellent communication and collaboration skills., * Experience with cloud platforms (e.g., Azure). * Familiarity with Snowflake and its integration with Foundry. * Exposure to Generative AI (GenAI) and its application in data workflows. * Knowledge of data security, compliance, and metadata management. ## Description You will play a pivotal role in shaping our data strategy, collaborating with stakeholders across the organization, and ensuring the integrity, performance, and cost-efficiency of our data ecosystem. While familiarity with Generative AI (GenAI) is a plus, your core expertise should lie in data architecture, pipeline development, and Foundry platform mastery., Data Architecture & Pipeline Engineering * Design, build, and maintain end-to-end ETL/ELT pipelines using PySpark, Spark SQL, and Python. * Architect scalable data frameworks that support high-performance analytics and operational workflows. * Conduct data cleansing, transformation, and validation to ensure data quality and consistency. Palantir Foundry Platform Expertise * Configure and manage data connections, Pipeline Builder, and Contour for data exploration and visualization. * Develop and maintain ontology objects, ensuring semantic consistency and reusability across applications. * Manage code repositories and version control within Foundry, promoting modular and maintainable engineering practices. Ontology-Aware Application Development * Collaborate with data owners and domain experts to ingest, transform, and model data into ontology-driven applications such as Workshop and Quiver. * Maintain clarity on the end-to-end architecture and data flow, ensuring seamless integration from ingestion to ontology. Cross-Functional Collaboration & Leadership * Partner with data scientists, analysts, and business stakeholders to translate requirements into scalable data solutions. * Engage with senior leadership to maintain a feedback loop for Foundry program improvements. * Provide mentorship and technical guidance to junior engineers and analysts. Operational Excellence & Cost Management * Establish and maintain cost monitoring and reporting for Foundry-related operations. * Lead efforts in troubleshooting, performance tuning, and platform stability. Continuous Improvement & Innovation * Stay current with Foundry platform updates, emerging features, and best practices. * Identify opportunities for automation, optimization, and innovation in data workflows. * Explore and evaluate GenAI capabilities for potential integration into data processes. #LI-RJ1 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Best Companies to work for in London: Top 25 Companies in 2023](https://www.wearedevelopers.com/magazine/187-best-companies-to-work-for-in-london-top-25-companies-in-2023) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)