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Mid-level role with popular data stack but niche Palantir Foundry requirement reduces candidate pool.
Core data engineering skills transferable, but Palantir Foundry and platform tooling increase specialization.
Explicit 6-8 years requirement plus mandatory PySpark, Foundry, AWS skills and unit testing.
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Develop, deploy, and maintain data integration and transformation solutions using Python, PySpark, AWS services, and Foundry platform components (Slate, Code Workbook).
Optimize data workflows and query performance leveraging Spark SQL and schedulers like Airflow within Agile frameworks.
Ensure solution reliability, security, and scalability through unit testing, test-driven development, and continuous communication with stakeholders.
3+ years of hands-on experience with Python, PySpark, and Spark SQL.
Experience with AWS technologies such as Lambda and RDS, and Foundry platform tools including Slate and Workbooks.
Bachelor's or Master’s degree in Computer Science, Computer Engineering, Information Technology, or a relevant field.
Total relevant development experience of 5-6 years, with familiarity in Agile Scrum, SAFe, or Kanban methodologies.
Experienced developer skilled across full stack Python and PySpark-based data pipelines and API integration.
Proficient in working with Foundry platform for building scalable, secure data and web solutions in fast-paced environments.
Demonstrated ability to optimize data processing using Spark-SQL, work effectively in Agile teams, and deliver against KPIs with clear stakeholder communication.