Manager – AI & Data Platform Engineering
Zscaler · Santa Clara, California, USA
About this role
**Manager – AI & Data Platform Engineering (Hybrid)** **About Zscaler** Zscaler (NASDAQ: ZS) accelerates digital transformation with the Zscaler Zero Trust Exchange—an AI-native, SASE-based in-line cloud security platform protecting thousands of customers from cyberattacks and data loss. **Role** Lead the evolution of Zscaler’s enterprise data platform into an **AI-ready data and knowledge platform**. This hands-on technical leadership role sits at the intersection of **Data Engineering, AI Platform Engineering, Knowledge Engineering, and Generative AI**, combining modern data engineering and cloud platform expertise with practical experience building **RAG systems, knowledge graphs, AI/LLM data pipelines, and production-grade AI infrastructure**. **What You’ll Do** - Lead a team of data and AI engineers across the US and India, owning performance, career development, and day-to-day execution (FT staff and contractors). - Own the Data Platform (**Snowflake, dbt, Matillion**) and in-house pipelines that build the enterprise’s knowledge and AI foundations—turning structured and unstructured data into trusted, AI-ready assets (e.g., **knowledge graphs, vector stores**) with accountability for quality, reliability, and delivery. - Translate platform strategy and architecture into roadmaps, quarterly plans, and delivered outcomes—partnering with architects and engineering leadership while shifting focus across data, knowledge, and AI as priorities change. - Run the platform as a product for internal customers (domain delivery teams, BI, context/semantic teams, and AI engineering), defining service levels, onboarding paths, and “paved roads” for fast and safe self-service and AI-native development. - Build **agentic operations** to automate operational work (pipeline monitoring, failure triage, data quality checks/remediation) and establish reusable frameworks/standards other teams can adopt. **Who You Are (Success Profile)** - Comfortable diving deep into architecture, removing roadblocks, and evaluating effort estimates while empowering senior/principal engineers to lead system design. - Understand the full engineering lifecycle from enterprise ingestion/transformation to knowledge retrieval, context engineering, and production agent deployment. - Able to drive architectural strategy and also execute technical implementation details; make pragmatic decisions to reduce technical debt. - Inspiring people leader focused on hiring, mentoring, and advancing engineers while building a culture of ownership and excellence. - Versatile across data platforms, knowledge engineering, and agentic operations; thrives in a fast-moving AI landscape. **Minimum Qualifications** - Practical understanding of **Generative AI architectures**, especially **RAG** and enterprise knowledge systems; experience designing pipelines for structured and unstructured data. - **10+ years** in software engineering, data engineering, data platform engineering, or data architecture. - **3+ years** leading engineering teams with demonstrated technical and people leadership. - Strong hands-on expertise in **Python** and **SQL**, with a foundation in modern data engineering and distributed systems. - Experience designing/operating production-grade data platforms on **AWS (or another major cloud)** using tools such as **Snowflake, Databricks, or BigQuery**, plus **dbt**. - Strong grounding in **data quality, metadata, lineage, governance, and observability**, with ability to
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