Palantir Foundry

Technology->Data Engineering->Palantir Foundry
Build, configure, and maintain data pipelines and workflows within Palantir Foundry to support business and analytics needs.
Collaborate with stakeholders to gather requirements, translate them into Foundry-based solutions, and ensure alignment with expected outcomes.
Perform data profiling, cleansing, and validation to improve data quality, consistency, and reliability across datasets.
Develop and maintain reusable components, transformations, and data models to enable scalable analytics and reporting.
Troubleshoot pipeline failures and performance bottlenecks; implement fixes and optimizations with clear documentation.
Support deployments, versioning, and controlled releases of Foundry assets while following governance and best practices.
Create clear technical documentation for pipelines, datasets, and operational procedures to support maintainability.
Work effectively in a hybrid setup, ensuring consistent communication, timely updates, and smooth coordination across teams Minimum Qualifications:
Education: BTECH, MCA, MSC, or MTECH.
3–5 years of overall experience in technology/data roles with hands-on delivery responsibilities.
Minimum 3 years of experience working with Palantir and Palantir Foundry in building data solutions.
Strong understanding of data pipeline concepts, data modeling, and data quality practices.
Ability to analyze requirements, communicate effectively with stakeholders, and deliver within agreed timelines.
Experience designing end-to-end Foundry solutions including dataset modeling, pipeline orchestration, and operational monitoring.
Working knowledge of SQL and Python for data transformation, automation, and analytical workflows integrated with Foundry.
Exposure to data governance practices such as access controls, lineage, and auditability in enterprise environments.
Familiarity with CI/CD concepts and version control to support reliable releases and collaborative development.
Strong problem-solving mindset with the ability to improve performance, reliability, and usability of data products.

Work arrangement
Hybrid