Palantir Data Platform Engineer

Project description

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.

• 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.

• 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 & Dev Ops:
  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 Dev Ops, Jira, Confluence, or similar tools.
• Educational Background:
  Degree in Computer Science, Mathematics, Statistics, Engineering, or related technical discipline.
• Financial Markets Knowledge:
  Familiarity with financial markets, portfolio theory, and risk management is a plus.

• Streaming Data Processing – Exposure to streaming data processing technologies like Apache Kafka for real‑time data ingestion and processing.
• Containerization – Knowledge of containerization technologies like Docker for creating, deploying, and running applications consistently across various environments.
• Data Modeling and Evaluation – Extensive experience in data modeling and the evaluation of large datasets.
• Model Training, Deployment, and Maintenance – Background in training, deploying, and maintaining models for effective data‑driven decision making.
• Requirements for Machine Learning – Experience in developing and implementing machine learning algorithms, Natural Language Processing (NLP), and Neural Networks.
• Applied Mathematics – Proficiency in applied mathematics, including but not limited to linear algebra, probability, statistics, and distributions.