Principal Software Engineer (Data Platform Prisma AIRS

Our Mission

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.

Who We Are

In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values:
Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real‑world problems and ideating beside the best and the brightest, we invite you to join us!

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.

Job Summary

As a Principal Software Engineer, Data Platform on the Prisma AIRS Runtime Security team, you will serve as a technical visionary and strategic leader shaping the future of our enterprise data infrastructure. You will partner with executive leadership, product management, ML engineers, data scientists, and cross‑functional software teams to conceptualize, architect, and deliver cutting‑edge, enterprise‑grade distributed processing solutions. You will be at the vanguard of innovation for our platform in the rapidly evolving space of real‑time streaming and high‑throughput batch data processing.

Your responsibilities will transcend traditional pipeline development to lead continuous performance and efficiency optimizations, enabling multi‑source data ingestion and transformation to power AI model lifecycle workflows, operational analytics, and automated security policy recommendations. You will drive technical excellence across the organization, set architectural standards, and act as a force multiplier by mentoring other engineers on the team.

• Partner with Product Management, SRE, ML Engineering, Security Researchers, and Quality Engineering to design and deliver high‑throughput data platform capabilities and streaming services.

• Architect and build resilient, multi‑source batch and streaming engines to power analytical workflows, business insights, and internal feature improvements.

• Profile, benchmark, and tune distributed execution DAGs, state storage, and compute resources to maximize throughput, lower processing latency, and optimize cloud expenditure at scale.

• Evangelize and implement modern software engineering best practices, including spec‑driven development, comprehensive testing frameworks, and agentic coding workflows.

• Act as a force multiplier by mentoring senior and junior engineers, conducting rigorous architectural reviews, and cultivating a culture of technical excellence across the team.

Qualifications

• Bachelor's degree in Computer Science or a related field with 10+ years of relevant industry experience, or a Master's degree with 8+ years of experience.

• Deep domain expertise in designing, building, and operating highly scalable, fault‑tolerant distributed systems, with exceptional programming skills in Golang and Python.

• Hands‑on experience building backend data platform infrastructure using distributed execution engines such as Apache Beam or Apache Spark.

• Hands‑on experience architecting, building, or deploying complex agentic workflows, tool‑use pipelines, or multi‑agent orchestrations within data infrastructure.

• Proven track record of profiling, benchmarking, and tuning high‑throughput distributed pipelines to improve latency and resource efficiency.

• Broad experience with cloud‑native data stacks (GCP/Dataflow preferred), messaging (Pub/Sub, Kafka), storage engines (Big Query, Iceberg), and infrastructure tooling (Kubernetes, Terraform).

• Demonstrated…