Distinguished Software Engineer (data Security - Distributed Systems, Big Data & AI)

Distinguished Engineer, Enterprise DLP

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.

Key responsibilities include:

I. Architecture & Strategic Vision

Define 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

II. High-Scale Data Platform Engineering

Big 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 Excellence

Technical 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

Preferred qualifications include:

12+ years of experience in a high-scale data-intensive environment, with a minimum of 3+ years operating as a Distinguished or Principal-level Engineer/Architect

Mastery of Google Cloud Platform (GCP) with extensive, hands-on experience architecting and scaling solutions using BigQuery and Vertex AI or equivalent AWS, Azure, or other Big Data & AI services

Expertise in Big Data processing frameworks and managed services, specifically with building and scaling data and analytics pipelines using Dataflow, Pub/Sub, and GKE (or equivalent technologies like Apache Spark/Kafka)

Strong experience in SQL & NoSQL databases (e.g., MongoDB, Cassandra, Spanner), with an understanding of their respective architectural trade-offs for distributed systems

Demonstrated ability to design scalable data models and systems that enable high-precision

Proven ability to build and optimize clean, well-structured analytical datasets for large-scale business and data science use cases

Demonstrated experience in implementing and supporting Big Data solutions for both batch (scheduled) and real-time (streaming) analytics

Prior experience in the security domain (especially DLP, Data Security, or Cloud Security) is a significant advantage

Exceptional ability to influence technical and business leaders, translating ambiguous problems into clear, executable technical designs

BS/MS in Computer Science or Electrical Engineering or equivalent experience or equivalent military experience required

Salary disclosure: The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be between $230,000/YR - $300,000/YR. The offered compensation may also include restricted stock units and a bonus.