Tejdeep Palem

Experience:

• Lead Data Engineer at Swiss Re, responsible for designing and architecting Big Data solutions for underwriting treaty property, processing ~40TB of property data.
• Built and optimized scalable data pipelines in Palantir Foundry, ingesting ~5–7GB of property data every 30 minutes, leveraging advanced external transforms to eliminate dependency on traditional ingestion tools.
• Designed and implemented incremental data processing workflows, ensuring efficient processing and minimal resource utilization.
• Architected solutions to sync processed data into Ontology Objects, enabling transactional and non-transactional reporting applications used by underwriters for portfolio acceptance and renewal decisions.
• Collaborated closely with underwriters to deliver tailored applications supporting both operational and analytical needs.
• Actively contributed to team development by mentoring and onboarding new engineers, reviewing and designing code, and implementing best practices to improve code quality and maintainability.
• Demonstrated expertise in Apache Spark, optimizing Spark jobs to handle large-scale data efficiently while minimizing costs and runtime.
• Leveraged the Palantir Foundry platform extensively, including Data Ingestion, Pipeline Builder, Ontology, Workshop, and Quiver, to build robust and user-centric data engineering applications.
• Ensured end-to-end solution quality by designing resilient architecture, conducting thorough testing, and implementing automated monitoring for data pipelines.
• Played a key role in aligning technical solutions with business goals, fostering collaboration between engineering and business teams.

Experience:

• Migrated an in-house Big Data platform utilizing Hadoop, Hive, and Spark managed by YARN to a hybrid cloud solution combining Snowflake, Databricks, and Palantir Foundry.
• Designed and implemented efficient data pipelines for processing large datasets (~10TB), ensuring high performance and scalability.
• Leveraged Snowflake for quick reporting solutions integrated with Power BI, enabling fast and efficient data-driven decision-making.
• Utilized Databricks for advanced processing of unstructured sensor data, optimizing Spark code for performance and resource efficiency.
• Built structured data processing workflows and user-facing applications in Palantir Foundry, leveraging tools such as Data Ingestion, Ontology, Pipeline Builder, Quiver, and Workshop for robust and scalable solutions.
• Skilled in optimizing Spark code, improving execution times, and reducing resource consumption across various Big Data workflows.
• Delivered applications in Palantir Foundry with write-back capabilities and custom TypeScript-backed widgets to enhance user interactivity and data materialization.
• Strong expertise in hybrid cloud architecture, enabling seamless integration of multiple technologies to meet diverse business needs.

Experience:

• Developed adaptive applications in C++ on the application layer of the Adaptive AUTOSAR framework, utilizing modern C++ standards (C++11/14/17) to enhance code efficiency and maintainability.
• Led a Proof of Concept (POC) project to install and remove applications on RCAR using OTA (Over-The-Air) updates based on Adaptive AUTOSAR.
• Designed and implemented multi-threaded algorithms to optimize application performance and resource utilization.
• Configured Essential IP files for IPC communication and deployment on virtual hardware (QEMU) and RCAR hardware.
• Debugged and resolved issues in the latest software releases, ensuring functionality compliance.
• Created software and vehicle packages, successfully transferring multiple SW packages through OTA and installing them on RCAR.
• Implemented UDS services such as Data Read by Identifier (0x22) and Routine Control (0x31) in automotive ECUs.