About the Job
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 acros...
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 ...
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 enterpr...
Key Responsibilities
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
Required Skills & Abilities
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.
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
#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.
Qualifications
Experience:
3 years experience
