Sr Manager, Enterprise Data Engineering

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

We are seeking a deeply technical Senior Manager to lead our data and machine learning engineering organization in India, reporting directly to the Director, Enterprise Data Engineering and Analytics. You will manage a talented team of data and ML engineers building foundational pipelines, lakehouse architectures, and MLOps platforms powering our global business.

As a technical player-coach, you will empower engineers to take end-to-end ownership of scalable, high-impact data products and AI solutions. This role requires high agency and a passion for driving enterprise-scale data and ML workflows.

Key Responsibilities

• Lead technical design and architecture of scalable enterprise data pipelines, lakehouse infrastructure, and end-to-end ML model development across key business domains, ensuring high availability and fault tolerance.
• Oversee deployment, serving, and scaling of machine learning models into production environments with robust MLOps practices, automated alerting, and continuous performance monitoring for drift and latency.
• Coach, mentor, and scale a high-performing team of data and ML engineers through structured technical guidance, code reviews, and career development, fostering engineering rigor and team autonomy.
• Partner closely with global data science, technical, and business leaders to translate complex analytical and strategic business needs into robust, production-grade technical workflows.
• Establish rigorous operational standards for data freshness, pipeline resilience, automated CI/CD for machine learning systems, and enterprise data governance.
• Foster an AI-first mindset across the organization, promoting generative AI tooling, automated workflows, and innovative engineering practices.
• Define sprint goals and establish roadmap prioritization for the team to align engineering execution with high-impact business outcomes.
• Track and enforce key data quality metrics, data lineage, SLA compliance, and observability frameworks across all data products.

• 12+ years of total experience in data engineering, MLOps
• 7+ years of experience directly managing data and/or machine learning engineering teams.
• Hands-on fluency in modern data platforms (especially GCP), distributed computing, and orchestration.
• Strong proficiency in core programming languages including Python and SQL.
• Extensive experience with the machine learning lifecycle, MLOps best practices, and relevant operational frameworks.

• Demonstrated player-coach mindset with the ability to dive into code reviews and ML architecture discussions.
• Proven track record of delivering high-throughput, enterprise-scale data and AI solutions.
• Experience collaborating across global time zones with US and international stakeholders.