Distinguished Software Engineer (DLP - Big Data & AI )

At Palo Alto Networks, we are redefining cybersecurity. As a Distinguished Engineer on the Enterprise DLP team, you will be the foremost technical leader responsible for architecting and scaling the data platform that underpins our industry-leading cloud-delivered DLP service. Your mission is to establish the standards and systems necessary to process and analyze massive volumes of sensitive data, leveraging cutting-edge AI/ML, to ensure our customers' data remains protected across all network, cloud, and user vectors.Your Impact & ResponsibilitiesAs a Distinguished Engineer, you will own the long-term technical direction and execution for all data and analytics infrastructure within Enterprise DLP.I. Architecture & Strategic VisionDefine Architectural Roadmap: Set the 3-5 year technical strategy and architectural vision for the Enterprise DLP data platform, emphasizing scalability, performance, security, and cost-efficiency.Big Data & AI Foundation: Drive the design, implementation, scaling, and evangelism of the core BigQuery, Vertex AI, Nvidia Triton, Kubeflow platform components that enable high-velocity data ingestion, transformation, and Machine Learning model serving for DLP detections.Real-time Decisioning: Architect and implement ultra-low latency data ingestion and processing systems (utilizing Kafka, Pub/Sub, Dataflow) to enable real-time DLP policy enforcement and alert generation at massive enterprise scale.Cross-Functional Influence: Act as the technical voice of the DLP data platform, collaborating with Engineering VPs, Product Management, and Data Science teams to align platform capabilities with product innovation.Detection Algorithm Enablement: Architect the core data structures and serving layers that enable high-performance DLP classification, like Regex, Exact Data Matching (EDM), Document Fingerprinting, and advanced ML/AI classifiers.II. High-Scale Data Platform EngineeringBig Data Pipeline Mastery: Architect and Lead the design and implementation of highly resilient, optimized batch and real-time data pipelines (ETL/ELT) to transform raw data streams into high-quality, actionable datasets.Optimized Datasets: Expertly design and optimize clean, well-structured analytical datasets within BigQuery, focusing on partitioning, clustering, and schema evolution to maximize query performance for both operational analytics and complex data science/ML feature generation.Database Strategy: Provide deep, hands-on expertise in both SQL and NoSQL databases like MongoDB, Spanner, BigQuery, advising on the optimal data persistence layer for diverse DLP data use cases (e.g., policy configurations, high-speed telemetry, analytical fact tables).MLOps Implementation: Establish robust MLOps practices model deployment & execution pipelines like Vertex AI, Nvidia Triton for DLP models, including automated pipelines for continuous training, versioning, deployment, and monitoring of model drift.Performance Engineering: Debug, optimize, and tune the most challenging performance bottlenecks across the entire data platform, from initial data ingestion to final analytics query execution, often dealing with PBs of data.III. Mentorship & Operational ExcellenceTechnical Mentorship: Mentor and develop Principal and Staff-level engineers, raising the bar for engineering craftsmanship and data platform development across the organization.Operational Health: Define and implement advanced observability, monitoring, and alerting strategies to ensure the end-to-end health and SLOs of the mission-critical DLP data service.