Manager – AI & Data Platform Engineering at Zscaler

Job description

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 technically strong, hands-on Manager – AI & Data Platform Engineering within the Enterprise Data & AI Platform Engineering department, based on a Hybrid (3 days in office) basis in Santa Clara, CA, and reporting directly to the Senior Manager – Data Strategy, to lead the evolution of our enterprise data platform into an AI-ready data and knowledge platform. Sitting at the intersection of Data Engineering, AI Platform Engineering, Knowledge Engineering, and Generative AI, you will combine a deep foundation in modern data engineering and cloud platforms with practical experience building RAG systems, knowledge graphs, AI/LLM data pipelines, and production-grade AI infrastructure. This is a hands-on technical leadership role spanning architecture, engineering execution, people leadership, and platform strategy. What You’ll Do (Role Expectations) Lead a team of data and AI engineers across the US and India, owning performance, career development and day-to-day execution across both full-time staff and contractors Own the Data Platform (Snowflake, dbt, Matillion) and the in-house pipelines that build the enterprise's knowledge and AI foundations, turning structured and unstructured data into trusted, AI-ready assets such as knowledge graphs and vector stores, accountable for quality, reliability and delivery Turn platform strategy and architecture direction into roadmaps, quarterly plans and delivered outcomes, partnering with architects and engineering leadership and shifting focus across data, knowledge and AI work as priorities change Run the platform as a product for internal customers such as domain delivery teams, BI, context and semantic teams and AI engineering, defining service levels, onboarding paths and paved roads that make self-service and AI-native development fast and safe Build agentic operations that automate the team's operational work, such as pipeline monitoring, failure triage, data quality checks and remediation, and establish a reusable framework and standards that other teams can adopt for their own operations Who You Are (Success Profile) You are comfortable diving deep into architectural discussions, removing roadblocks for team members, and evaluating effort estimates effectively, all while empowering your senior and principal engineers to lead system design. You possess a comprehensive understanding of the complete engineering lifecycle from enterprise data ingestion and transformation to knowledge retrieval, context engineering, and production agent deployment. You are equally adept at driving architectural strategy and engaging in technical implementation details, making pragmatic decisions to eliminate technical debt. You are an inspiring people leader committed to hiring, mentoring, and advancing engineering team members while cultivating a culture of ownership and engineering excellence. Versatile across data platforms, knowledge engineering and agentic operations, you’re equally at home in any of the three. You lead a globally distributed team with an owner’s mindset and a bias for action, and you thrive in a fast-moving AI landscape. What We're Looking For (Minimum Qualifications) Practical understanding of Generative AI architectures, particularly RAG and enterprise knowledge systems, with experience designing pipelines for structured and unstructured data 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 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, with a proven ability to translate ambiguous problems into scalable engineering solutions What Will Make You Stand Out (Preferred Qualifications) Production experience building RAG or GraphRAG platforms, Knowledge Graphs, and graph databases (ArangoDB, Neo4j, Amazon Neptune), 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, alongside LLM evaluation, AI observability, and LLMOps Deep cloud-native platform engineering expertise across Kubernetes, Terraform, and Ray/Ray Data, with a track record of building internal developer platforms and reusable data and AI platform services #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. Base Pay Range $164,000 — $205,000 USD 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.

Requirements

Must-haves

What You’ll Do (Role Expectations)
Lead a team of data and AI engineers across the US and India, owning performance, career development and day-to-day execution across both full-time staff and contractors
Who You Are (Success Profile)
You possess a comprehensive understanding of the complete engineering lifecycle from enterprise data ingestion and transformation to knowledge retrieval, context engineering, and production agent deployment.
You are equally adept at driving architectural strategy and engaging in technical implementation details, making pragmatic decisions to eliminate technical debt.
You are an inspiring people leader committed to hiring, mentoring, and advancing engineering team members while cultivating a culture of ownership and engineering excellence.
What We're Looking For (Minimum Qualifications)
Practical understanding of Generative AI architectures, particularly RAG and enterprise knowledge systems, with experience designing pipelines for structured and unstructured data
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
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, with a proven ability to translate ambiguous problems into scalable engineering solutions
Production experience building RAG or GraphRAG platforms, Knowledge Graphs, and graph databases (ArangoDB, Neo4j, Amazon Neptune), 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, alongside LLM evaluation, AI observability, and LLMOps
Deep cloud-native platform engineering expertise across Kubernetes, Terraform, and Ray/Ray Data, with a track record of building internal developer platforms and reusable data and AI platform services
Various health plans
Time off plans for vacation and sick time
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.

Nice-to-haves

What Will Make You Stand Out (Preferred Qualifications)

Application prep

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Keywords to mirror

agileaillmpythonsqlawskubernetes

Platform: greenhouse
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Benefits

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