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# GenAI Data Engineer

- **Company:** Zscaler, Inc.
- **Location:** Bellevue, WA, United States
- **Salary:** $185,500 - $265,000
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Training Data, Artificial Intelligence, Software as a Service, Collaborative Software, Databases, Data Architecture, Data Infrastructure, Graph Database, Python (Programming Language), Knowledge-Based Systems, Machine Learning, Metadata, Software Engineering, Unstructured Data, Data Ingestion, Retrieval-Augmented Generation, Large Language Models, Generative AI, Agentic-AI, Data Strategy, LangSmith, Information Technology, CrewAI, Arize Phoenix, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** November 13, 2026
- **Apply:** https://startup.jobs/principal-genai-data-engineer-zscaler-company-10755076

## About the Role

We are seeking an experienced technical leader to drive the design and implementation of enterprise-grade Generative AI data ingestion, knowledge preparation, and platform architectures that enable scalable, production-ready GenAI applications. This role focuses on architecting robust pipelines and platforms for ingesting, processing, governing, and serving structured and unstructured enterprise data for AI/LLM workloads. The ideal candidate combines deep expertise in enterprise data architecture, unstructured data pipelines, GenAI platform engineering, and strong software engineering skills in Python., * You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.

 * Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.
 * You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.
 * You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback-knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.
 * You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose., * Foundational understanding of AI/ML technologies and experience leveraging, securing, or positioning AI-driven solutions to optimize outcomes within your functional domain
 * Expert-level Python programming and software engineering capabilities
 * Experience building distributed/scalable data pipelines for AI workloads
 * Strong understanding of unstructured data extraction and processing pipelines
 * Experience with vector databases, graph databases, and metadata/knowledge storage systems
 * Hands-on experience with clustering, entity recognition algorithms, and modern retrieval strategies (including RAG, search, and agentic AI workflows)

What Will Make You Stand Out

 * Advanced experience architecting real-time distributed vector search infrastructure and multi-modal knowledge graph pipelines for enterprise-grade Retrieval-Augmented Generation (RAG) applications
 * Experience with LLMOps / GenAIOps frameworks such as LangSmith, Evaluation Framework like Arize Phoenix, Weights & Biases, or MLflow
 * Familiarity with Agent Frameworks like LangGraph, CrewAI, or Google ADK

## Description

We are looking for a Principal GenAI Data Engineer to join our team. This is a Hybrid role based in Santa Clara, CA or Bellevue, WA (3 days in office), reporting to the Senior Manager, Enterprise AI Data Platform in the Information Technology Data Strategy department., * Architect enterprise-scale GenAI data platforms for ingestion, transformation, enrichment, and serving of structured and unstructured data

 * Design scalable pipelines for enterprise knowledge ingestion from diverse data sources including documents, SaaS platforms, knowledge bases, collaboration tools, and databases
 * Define architecture for metadata extraction, chunking, enrichment, embeddings generation, and knowledge preparation workflows
 * Design AI-ready data models and storage strategies for vector, graph, and hybrid knowledge systems
 * Architect scalable unstructured data processing pipelines for text, images, PDFs, tables, and multimodal content