Senior Palantir Data Platform Engineer

We are actively seeking a talented Palantir Data Platform Engineer with strong proficiency in Python programming, modern data engineering practices, and cloud-based platforms. The ideal candidate will have experience building scalable data pipelines, working with distributed data systems, and developing solutions using Palantir Foundry and Databricks. Experience with cloud platforms such as Azure or AWS, CI/CD processes, APIs, and data integration frameworks will be highly valued.

• Responsibilities:

  Participate in requirements clarification and sprint planning sessions.
  Design, develop, and maintain scalable data pipelines and solutions within Palantir Foundry aligned with business and project goals.
  Build and optimize data ingestion, transformation, and integration workflows using Python, SQL, and PySpark.
  Develop unit and integration tests to ensure reliability and quality of data pipelines and platform components.
  Support deployment, monitoring, and troubleshooting of data workflows across cloud and distributed environments.
  Collaborate with QA engineers and stakeholders during testing and acceptance phases, resolving issues promptly.
  Continuously improve best practices for data engineering, CI/CD, code quality, and platform performance.
  Work closely with cross-functional teams including data scientists, analysts, architects, and developers to ensure efficient delivery.
  Stay up to date with Palantir Foundry, cloud technologies, and modern data engineering best practices.
  Contribute to technical documentation and actively participate in code reviews and knowledge sharing.

• Mandatory Skills Description:

  5+ years of relevant experience in a Senior Data Engineer or Palantir Data Platform Engineer role.
  Hands-on experience with Palantir Foundry, including data pipelines, ontology, transformations, and workflow orchestration.
  Big Data Technologies: Familiarity with Hadoop, Apache Spark, PySpark, or other distributed computing frameworks.
  Data Security and Governance: Strong understanding of data security, governance, data lineage, data quality, and compliance best practices.
  Python and PySpark: Strong expertise in Python and PySpark for scalable data processing and analytics.
  Advanced SQL Knowledge: Ability to write and optimize complex SQL queries and database operations.
  ETL Experience: Proven experience designing and supporting ETL/ELT processes and enterprise data workflows.
  Data Pipelines: Experience with data cleansing, profiling, lineage, and scalable pipeline development.
  API Integration: Experience integrating data pipelines with REST/SOAP APIs and external systems.
  Python Libraries: Familiarity with building reusable Python libraries and frameworks.
  Version Control: Proficiency with Git and collaborative development workflows.
  Cloud Technology Experience: Experience with Azure, AWS, or other leading cloud platforms.
  CI/CD & DevOps: Familiarity with CI/CD pipelines, automation, and deployment best practices.
  Data Visualization: Exposure to Power BI, Tableau, or similar reporting/visualization tools.
  Collaboration Tools: Experience with Azure DevOps, Jira, Confluence, or similar tools.
  Educational Background: Degree in Computer Science, Mathematics, Statistics, Engineering, or related technical discipline.