Pyspark/Palantir

AIA Coimbatore

Experience : 6 to 9 years

Job Summary

Contribute as a senior developer specializing in PySpark and Palantir Foundry to build scalable data pipelines and analytical solutions within a global enterprise environment. Collaborate with cross functional teams in a hybrid work setup to transform complex business requirements into reliable data products that improve decision making and operational efficiency.

Responsibilities

• Design robust PySpark data pipelines that reliably process large scale structured and unstructured datasets to enable accurate reporting and analytics for business stakeholders.
• Develop optimized transformations in PySpark that improve runtime performance and resource utilization while maintaining high standards of data quality and consistency.
• Implement modular data workflows in Palantir Foundry that integrate diverse enterprise data sources and provide curated datasets for downstream applications.
• Configure and manage datasets transformations and schedules in Palantir Foundry to ensure that critical data assets remain fresh traceable and well documented.
• Collaborate with product owners data analysts and other developers to translate business requirements into clear technical specifications and reusable data components.
• Conduct detailed code reviews for PySpark and Foundry transformation logic to uphold coding standards improve maintainability and reduce production issues.
• Troubleshoot complex data pipeline incidents by performing root cause analysis and implementing sustainable fixes that prevent recurrence and protect service reliability.
• Optimize data models and query patterns so that analytical and operational dashboards perform efficiently and deliver timely insights to decision makers.
• Document data lineage business rules and transformation logic in a clear and accessible manner so that teams across the organization can confidently reuse shared data assets.
• Partner with platform and infrastructure teams to ensure that Spark cluster configurations job schedules and resource allocations align with performance and cost objectives.
• Apply secure coding and data handling practices to safeguard sensitive information and comply with internal policies and external regulatory expectations.
• Provide mentoring and guidance to less experienced developers on PySpark patterns testing approaches and best practices for building reliable data solutions.
• Engage in continuous improvement activities by evaluating new features in Palantir Foundry and Spark technology ecosystems to enhance the resilience and scalability of existing solutions.
• Coordinate with testing teams to define test data strategies validation rules and automated checks that verify correctness of complex data transformations before production deployment.
• Communicate progress risks and technical constraints clearly to project stakeholders so that delivery timelines and scope can be managed effectively.
• Partner with business teams to identify opportunities where advanced data engineering on Spark and Foundry can streamline processes and create measurable value for customers and communities.
• Ensure that hybrid working practices are effective by using collaboration tools regular check ins and documented workflows that support both in office and remote team members.
• Align daily development activities with the organization mission by focusing on data capabilities that drive better services improved sustainability and responsible use of technology.
  
  

Qualifications

• Demonstrate six to eight years of hands on experience in designing and implementing data engineering solutions using PySpark in large scale enterprise environments.
• Exhibit strong proficiency in Palantir Foundry including building transformations managing datasets configuring schedules and integrating with upstream and downstream systems.
• Apply solid understanding of distributed data processing concepts such as partitioning caching and shuffle optimization to tune PySpark jobs for performance.
• Use practical knowledge of SQL and data modeling to design schemas joins and aggregations that support both analytics and operational use cases with high data quality.
• Show proficiency in version control and collaborative development practices so that changes to PySpark and Foundry assets are traceable reviewable and reversible.
• Employ experience with testing frameworks and data validation techniques to establish automated checks that secure reliability of production data pipelines.
• Display excellent communication and collaboration skills that enable effective work with cross functional teams in a hybrid work environment without requiring frequent travel.