Zscaler (NASDAQ: ZS) accelerates digital transformation so customers can be more agile, efficient, resilient, and secure. The Zscaler Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location. Distributed across 200+ public data centers globally and thousands of private sites at the edge, the SASE-based Zero Trust Exchange is the world’s largest in-line cloud security platform.
We believe the future of work is Human + AI and are building an AI-native enterprise where human potential is amplified by machine intelligence to solve the world’s hardest security challenges. Driven by deep customer obsession, we are committed to the mission, outcome, and to each other. We bring these commitments to life through three core behaviors: ownership and collaboration, trust through outcomes and impact, and a challenge culture with ongoing feedback. Ready to make an impact at the company pioneering security transformation in the AI era? Join us at Zscaler.
Role
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.
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.
What You’ll Do
- 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
Who You Are
- 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.
What We’re Looking For
- 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
#LI-YC2 #LI-Hybrid
Zscaler’s salary ranges are benchmarked and are determined by role and level. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations and could be higher or lower based on a multitude of factors, including job-related skills, experience, and relevant education or training.
The base salary range listed for this full-time position excludes commission/ bonus/ equity (if applicable) + benefits.
At Zscaler, we are committed to building a team that reflects the communities we serve and the customers we work with. We foster an inclusive environment that values all backgrounds and perspectives, emphasizing collaboration and belonging. Join us in our mission to make doing business seamless and secure.
Our Benefits program is one of the most important ways we support our employees. Zscaler proudly offers comprehensive and inclusive benefits to meet the diverse needs of our employees and their families throughout their life stages, including:
- Various health plans
- Time off plans for vacation and sick time
- Parental leave options
- Retirement options
- Education reimbursement
- In-office perks, and more!
Learn more about Zscaler's hybrid working model and benefits here.
By applying for this role, you adhere to applicable laws, regulations, and Zscaler policies, including those related to security and privacy standards and guidelines.
Zscaler is committed to providing equal employment opportunities to all individuals. We strive to create a workplace where employees are treated with respect and have the chance to succeed. All qualified applicants will be considered for employment without regard to race, color, religion, sex (including pregnancy or related medical conditions), age, national origin, sexual orientation, gender identity or expression, genetic information, disability status, protected veteran status, or any other characteristic protected by federal, state, or local laws. See more information by clicking on the Know Your Rights: Workplace Discrimination is Illegal link.
Pay Transparency
Zscaler complies with all applicable federal, state, and local pay transparency rules.
Zscaler is committed to providing reasonable support (called accommodations or adjustments) in our recruiting processes for candidates who are differently abled, have long term conditions, mental health conditions or sincerely held religious beliefs, or who are neurodivergent or require pregnancy-related support.
Skills Required
- Foundational understanding of AI and machine learning technologies, including experience leveraging, securing, or positioning AI-driven solutions
- Expert-level Python programming and software engineering capabilities
- Experience building distributed and scalable data pipelines for AI workloads
- Strong understanding of unstructured data extraction and processing pipelines
- Experience with vector databases, graph databases, and metadata or knowledge storage systems
- Hands-on experience with clustering, entity recognition algorithms, and modern retrieval strategies including RAG, search, and agentic AI workflows
- Advanced experience architecting real-time distributed vector search infrastructure and multimodal knowledge graph pipelines for enterprise RAG applications
- Experience with LLMOps or GenAIOps frameworks such as LangSmith, Arize Phoenix, Weights & Biases, or MLflow
- Familiarity with agent frameworks such as LangGraph, CrewAI, or Google ADK
Zscaler Compensation & Benefits Highlights
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Healthcare Strength — Health, dental and vision plans through major carriers, alongside mental-health services, virtual care, and fertility resources, create broad coverage. Feedback suggests coverage breadth and provider choice are seen as strong points.
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Parental & Family Support — Paid parental bonding leave for birthing and non-birthing parents, plus additional disability leave and a phased return option, signals meaningful family support. Feedback suggests these policies contribute to a family-friendly package.
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Equity Value & Accessibility — An ESPP with at least a 15% discount and RSUs for eligible roles add wealth-building potential beyond base pay. Feedback suggests stock programs are a notable strength of the total rewards mix.
Zscaler Insights
What We Do
Zscaler accelerates digital transformation so our customers can be more agile, efficient, resilient, and secure. Our cloud native Zero Trust Exchange platform protects thousands of customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.
Why Work With Us
Our impact comes from how we work—every day, in every decision, as one team. Our values and leadership principles are more than words; they're our operating system. They fuel a culture of execution where trust, transparency, and accountability help us deliver meaningful results for our customers, colleagues, and our company. www.zscaler.com/culture
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Zscaler Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Zscaler's hybrid work environment balances individual employee work styles with best practices to build trust and collaboration – all in support of addressing personal productivity and achieving organizational success.
