Principal Engineer – AI​/ML Analytics Platform & Cloud Security

About Netskope

Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security.

Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork.

From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive. Visit us at Netskope Careers. Please follow us on Linked In and Twitter Netskope.

We are seeking a Principal Engineer to architect and lead the development of an advanced AI-powered analytics platform that combines machine learning, natural language interfaces, and large-scale data systems. This role is at the forefront of Netskope’s innovation in cloud security intelligence, enabling our customers to derive real-time insights and automated decisions from massive volumes of telemetry and event data.

The strategic and technical leadership role requires broad systems expertise, product thinking, and a strong ability to collaborate across engineering, product, security, and AI teams. You will drive initiatives that intersect search-driven analytics (like Thought Spot), LLM-powered workflows (like Upsolve AI), and real-time cloud security analytics.

• Define and drive the architecture for an AI analytics platform that supports natural language queries, visual analytics, and ML-assisted insights across security data.
• Lead the integration of LLMs and Retrieval-Augmented Generation (RAG) into interactive analytics flows, enabling context-rich user experiences.
• Own the design and development of high-performance data systems for querying, indexing, and streaming large-scale telemetry and behavioral data.
• Drive backend platform scalability, availability, and observability across core analytics and ML services.
• Partner with security, data science, and product teams to prioritize use cases, define technical strategy, and influence roadmap.
• Establish engineering best practices in system design, API architecture, performance tuning, data modeling, and ML platform integration.
• Mentor senior engineers and foster a high-bar engineering culture grounded in innovation, ownership, and execution.
• Represent the engineering vision in cross-functional strategy discussions, architectural reviews, and external technical forums if needed.

Core Technical Expertise

• 15+ years of experience building scalable, distributed systems for data analytics, ML, or search-based platforms.
• Proven track record of architecting and delivering end-to-end AI or analytics platforms (BI tools, data apps, or ML-driven insights platforms).
• Deep expertise in backend engineering using Python, Java, or Scala; advanced proficiency in SQL and performance optimization.
• Experience designing streaming and batch data pipelines using tools like Spark, Kafka, Flink, or equivalent.
• Hands-on experience with MLOps platforms and modern ML deployment workflows (e.g., MLflow, Kubeflow, Airflow).
• Strong understanding of LLMs and vector databases (e.g., Pinecone, PGVector) and their application in semantic search and insight generation.
• Deep understanding of data modeling for analytical systems (star/snowflake schemas, OLAP, dimensional modeling).

Platform & Product Experiences

• Demonstrated success in building platforms that power user-facing experiences like dashboards, alerts, or search interfaces (e.g., Thought Spot, Looker, or similar).
• Experience working with modern cloud platforms (AWS, GCP, Azure) and big data storage engines (Big Query, Click House, Snowflake).
• Proven ability…