Manager – AI & Data Platform Engineering

Zscaler•Santa Clara, CA

4d•Hybrid

About The Position

Zscaler is seeking a technically strong, hands-on Manager – AI & Data Platform Engineering within its Enterprise Data & AI Platform Engineering department. The role is based in Santa Clara, California, on a hybrid schedule requiring three days in the office, and reports directly to the Senior Manager – Data Strategy. The manager will lead the evolution of the enterprise data platform into an AI-ready data and knowledge platform. The role spans 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. It includes architecture, engineering execution, people leadership, and platform strategy. The role leads a globally distributed team of data and AI engineers in the US and India, including full-time employees and contractors. It owns the data platform and in-house pipelines that convert structured and unstructured data into trusted, AI-ready assets. The manager also translates platform strategy into roadmaps and delivered outcomes, operates the platform as a product for internal customers, and develops reusable agentic operations frameworks.

Requirements

Practical understanding of generative AI architectures, particularly RAG and enterprise knowledge systems, and experience designing pipelines for structured and unstructured data.
At least 10 years of experience in software engineering, data engineering, data platform engineering, or data architecture.
At least 3 years of experience leading engineering teams, with demonstrated technical and people leadership.
Strong hands-on expertise in Python and SQL, with a solid foundation in modern data engineering and distributed systems.
Experience designing and operating production-grade data platforms on AWS or another major cloud platform, using modern tools such as Snowflake, Databricks, or BigQuery, along with dbt.
Strong grounding in data quality, metadata, lineage, governance, and observability.
Proven ability to translate ambiguous problems into scalable engineering solutions.

Nice To Haves

Production experience building RAG or GraphRAG platforms, knowledge graphs, and graph databases such as ArangoDB, Neo4j, or Amazon Neptune.
Experience with vector databases, embeddings, hybrid retrieval, and re-ranking.
Hands-on experience building infrastructure for AI agents and agentic workflows using frameworks such as LangGraph, LangChain, or LlamaIndex.
Experience with LLM evaluation, AI observability, and LLMOps.
Deep cloud-native platform engineering expertise across Kubernetes, Terraform, and Ray/Ray Data.
Track record of building internal developer platforms and reusable data and AI platform services.

Responsibilities

Lead a team of data and AI engineers across the US and India, managing performance, career development, and day-to-day execution for full-time staff and contractors.
Own the Snowflake, dbt, and Matillion data platform and the pipelines that build enterprise knowledge and AI foundations.
Turn structured and unstructured data into trusted, AI-ready assets such as knowledge graphs and vector stores, with accountability for quality, reliability, and delivery.
Translate platform strategy and architecture direction into roadmaps, quarterly plans, and delivered outcomes in partnership with architects and engineering leadership.
Adjust the team's focus across data, knowledge, and AI work as priorities change.
Run the platform as a product for internal customers, including domain delivery teams, BI, context and semantic teams, and AI engineering; define service levels, onboarding paths, and paved roads for safe, fast self-service and AI-native development.
Build agentic operations that automate work such as pipeline monitoring, failure triage, data quality checks, and remediation.
Establish reusable frameworks and standards for agentic operations that other teams can adopt.