Netskope is seeking to redefine cloud, network, and data security by building an advanced AI-powered analytics platform that combines machine learning, natural language interfaces, and large-scale data systems to provide customers with real-time insights and automated decisions from massive volumes of telemetry and event data.
Requirements
- 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).
Responsibilities
- 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.
Other
- Exceptional communication and collaboration skills across functions—engineering, product, data science, and executive stakeholders.
- Ability to define and influence architectural direction at an organizational level.
- Experience mentoring staff- and senior-level engineers and setting long-term engineering strategies.
- Prior experience in security analytics, threat detection, or operationalizing security data at scale.
- Exposure to natural language query systems or AI copilots (e.g., NL2SQL, prompt engineering, question-answering).
