Manager – AI & Data Platform Engineering

Zscaler•Santa Clara, CA

6h•$164,000 - $205,000•Hybrid

About The Position

Zscaler is seeking a technically strong, hands-on Manager – AI & Data Platform Engineering to lead the evolution of its enterprise data platform into an AI-ready data and knowledge platform. The 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 in RAG systems, knowledge graphs, AI/LLM data pipelines, and production-grade AI infrastructure. It is a hands-on technical leadership position spanning architecture, engineering execution, people leadership, and platform strategy. The position is hybrid, with three days in the office in Santa Clara, California, and reports to the Senior Manager – Data Strategy. The manager will lead a globally distributed team of data and AI engineers across the US and India, and own the enterprise data platform and internal pipelines that create knowledge and AI foundations. The role is accountable for turning structured and unstructured data into trusted, AI-ready assets and for delivering a reliable platform that serves internal engineering and analytics teams. The manager will translate strategy into roadmaps and outcomes, run the platform as a product, and build reusable agentic operations to automate operational work. The successful candidate will combine architectural judgment and implementation-level technical depth with effective people leadership. They will guide and develop engineers, support senior and principal engineers in system design, make pragmatic decisions to reduce technical debt, and operate effectively in a fast-moving AI environment.

Requirements

10+ years of experience in software engineering, data engineering, data platform engineering, or data architecture.
3+ years of experience leading engineering teams, with demonstrated technical and people leadership.
Practical understanding of Generative AI architectures, particularly RAG and enterprise knowledge systems, and experience designing pipelines for structured and unstructured data.
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.
Ability to translate ambiguous problems into scalable engineering solutions.
Understanding of the engineering lifecycle from enterprise data ingestion and transformation through knowledge retrieval, context engineering, and production agent deployment.
Ability to lead a globally distributed team and balance architectural strategy with technical implementation details.

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.
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 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 Data Platform (Snowflake, dbt, Matillion) and in-house pipelines that create the enterprise’s knowledge and AI foundations.
Turn structured and unstructured data into trusted, AI-ready assets, including knowledge graphs and vector stores, and ensure quality, reliability, and delivery.
Translate platform strategy and architecture direction into roadmaps, quarterly plans, and delivered outcomes, partnering with architects and engineering leadership and adjusting priorities across data, knowledge, and AI work.
Operate 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 that support fast, safe self-service and AI-native development.
Build agentic operations to automate work such as pipeline monitoring, failure triage, data quality checks, and remediation.
Establish a reusable framework and standards for agentic operations that other teams can adopt.
Provide technical leadership in architecture discussions, remove roadblocks, evaluate effort estimates, and empower senior and principal engineers to lead system design.
Hire, mentor, and advance engineering team members while cultivating ownership and engineering excellence.