Prateek Jain

Principal Data Engineer @Munich Re

Mountain View, CA, US
EMAILS
p••••@munichre.com
MOBILE NUMBERS
+16•••••••97

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WORK HISTORY

Jun 2025 — Present

Principal Data Engineer @Munich Re

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Define and execute data strategy for the U.S. data engineering organization, establishing coding standards, best practices, and workflows for consistent, scalable delivery- Architect and automate data pipelines using Azure Data Factory, Databricks, and Azure Data Lake, enabling faster access to critical data and improving processing efficiency- Spearheaded migration of 10+ ETL pipelines from on-premise to Azure Cloud, enhancing data security, scalability, and reliability- Developed custom web-based analytics tools (Flask, PHP, ChartJS) that replaced Power BI for internal reporting, reducing licensing costs and providing real-time insights- Designed a unified data integration framework that automated data consolidation for actuarial teams, cutting manual work by 60% and improving data accuracy- Optimized large-scale data ingestion using distributed computing in PySpark, improving throughput and scalability for terabyte-scale datasets- Lead architectural decisions across projects, including DevOps setup for Databricks pipelines, alerting frameworks, and test automation standards using pytest.

EDUCATION

2010 — 2014

Punjab Engineering College

Bachelor of Engineering (B.E.), Computer Science

2017 — 2019

Rutgers University

Master's of Business and Science, Data Science and Analytics

SKILLS

C++ LanguageEclipseHtmlSoftware DevelopmentPythonScikit-LearnJavaWeb DevelopmentMicrosoft OfficeSparkCSqlC#NetbeansScala

ABOUT PRATEEK JAIN

Results-driven Principal Data Engineer with 9+ years of experience building and leading scalable cloud data platforms. Expert in architecting data pipelines on Azure, Databricks, and Spark, defining engineering standards, and delivering high-performance data solutions that enable real-time analytics and business agility. Proven record of leading global data initiatives, driving cloud migration, and optimizing large-scale data operations.

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