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

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

At Palo Alto Networks®, we're united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you're ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you're in the right place.

We believe collaboration thrives in person. That's why most of our teams work from the office full time, with flexibility when it's needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.

You'll be working in a top tier cybersecurity company and collaborating with some of the brightest minds in technology. Our team doesn't shy away from tackling big problems. You will help build and support the tools and infrastructure enabling our developers to release the products that our customers depend on to defend against cyberattacks. Joining this dynamic and fast-paced team will give you the opportunity and thrill of resolving the technical and process gaps that hold back productivity.

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.

As a Distinguished Engineer, you will own the long-term technical direction and execution for all data and analytics infrastructure within Saas Data Security and AI.

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

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

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

• 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