About the role
About Us: Proofpoint is a global leader in human- and agent-centric cybersecurity. We protect how people, data, and AI agents connect across email, cloud, and collaboration tools. Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows. Our mission is simple: safeguard the digital world and empower people to work securely and confidently. Join us in our pursuit to defend data and protect people.
How We Work: At Proofpoint you’ll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values: Bold in how we dream and innovate Responsive to feedback, challenges and opportunities Accountable for results and best in class outcomes Visionary in future focused problem-solving Exceptional in execution and impact
The Role We're seeking a Senior Data Engineer to build and maintain the ML/AI data infrastructure powering our email security platform. In this role, you'll design and optimize scalable data pipelines that enable threat detection and investigation while supporting both machine learning models and LLM-powered agents that provide context-aware security insights. You'll work on our Detection Intelligence Platform (DIP) building feature engineering frameworks, and offline/online feature stores that serve as the foundation for ML model research and context engineering for AI agents. You'll collaborate with data scientists, ML engineers, and security researchers to build data models and context stores that power our detection systems and enable human security analysts to investigate threats effectively. Key
Responsibilities: Develop and maintain scalable data pipelines on AWS/Azure using technologies such as Spark, Airflow, Athena, Kubernetes etc. to process structured and unstructured email data at scale Design and optimize Iceberg-based data lake tables and schemas for efficient storage, querying and versioning across petabyte-scale datasets distributed across data centers globally Build and manage feature engineering frameworks that support offline batch processing and online real-time feature serving for ML model training and inference Develop and maintain training data pipelines optimized for distributed ML model training, ensuring data lineage and reproducibility Collaborate with data scientists and security researchers to understand data
