Staff IT Software Engineer

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

Staff Data Engineer - Marketing Technology

The Role

Palo Alto Networks is building a modern data platform to support real-time, AI-enabled, and scalable go-to-market systems. As a Staff Data Engineer within Marketing Technology, you will design and build the data foundation that powers workflows, decisioning, analytics, and downstream activation across the GTM stack.

This is a hands-on engineering role. You will work on streaming pipelines, workflow orchestration, data models, and platform reliability. You are expected to operate across batch and real-time systems, partner closely with product and platform engineers, and build systems that are observable, resilient, and easy to extend.

This is not a reporting-focused data role. This is about building production-grade data systems that other systems and workflows depend on.

Responsibilities

• Design and build scalable data pipelines across batch and real-time systems to support marketing, analytics, and AI-driven workflows
• Develop and maintain streaming data platforms (for example Kafka, Kinesis, Pub/Sub) to enable event-driven architectures
• Build and manage workflow orchestration using tools such as Airflow or equivalent
• Design clean, well-structured data models that support multiple use cases including analytics, activation, and AI systems
• Implement data contracts and enforce schema consistency across producers and consumers
• Partner with application and platform engineers to integrate data systems with APIs, services, and downstream workflows
• Ensure high data quality through validation, monitoring, alerting, and automated checks
• Optimize pipelines for performance, cost, and reliability at scale
• Contribute to platform-level capabilities such as reusable pipelines, shared datasets, and developer tooling
• Support AI and automation use cases by enabling reliable access to structured and unstructured data

• 4 or more years of experience in data engineering, with strong hands-on experience building data platforms
• Strong programming skills in Python, with experience building production-grade data pipelines
• Experience building and operating streaming systems using technologies such as Kafka, Kinesis, or Pub/Sub
• Strong experience with workflow orchestration tools such as Airflow (or similar DAG-based systems)
• Deep understanding of data modeling, including designing datasets for both analytical and operational use cases
• Experience working with distributed data processing frameworks (for example Spark, Flink, or similar)
• Drive the automation of the data lifecycle by implementing robust CI/CD pipelines and Infrastructure as Code (IaC) to standardize deployments and accelerate engineering velocity.
• Strong SQL skills and ability to work directly with large-scale datasets
• Experience with cloud platforms such as AWS, GCP, or Azure
• Familiarity with event-driven architectures and asynchronous data processing patterns
• Experience building systems with strong observability, including logging, monitoring, and alerting

• Experience building data platforms that support AI or machine learning workflows
• Familiarity with real-time feature generation, event enrichment, or low-latency data processing
• Experience with modern data stack tools and patterns (for example dbt, warehouse-native transformations, or similar)
• Exposure to data governance, lineage tracking, and schema evolution practices
• Experience working in MarTech, AdTech, or customer data platforms
• Understanding of privacy, compliance, and data handling requirements in enterprise environments