Manager – AI & Data Platform Engineering

Description

The company makes a cloud security platform, the Zero Trust Exchange, which connects users, devices and applications from any location and protects customers from cyberattacks and data loss. It runs across more than 200 public data centers worldwide plus thousands of private edge sites, and is described as the largest in-line cloud security platform of its kind.

The company is building an AI-native enterprise where people work alongside machine intelligence. Ownership, collaboration, customer-focused outcomes and open challenge are central to how the team works.

This is a hybrid role based in Santa Clara, CA, with 3 days per week in the office. The manager sits in the Enterprise Data and AI Platform Engineering department and reports directly to the Senior Manager of Data Strategy.

The role leads the move of the enterprise data platform toward an AI-ready data and knowledge platform. It sits where data engineering, AI platform engineering, knowledge engineering and generative AI overlap, and it combines hands-on technical work with architecture, team leadership and platform strategy.

Responsibilities

• Lead a distributed team of data and AI engineers in the US and India, both full-time staff and contractors, covering performance, career growth and daily delivery.
• Own the data platform (Snowflake, dbt, Matillion) and the internal pipelines that create the company's knowledge and AI foundations. Turn structured and unstructured data into trusted, AI-ready assets such as knowledge graphs and vector stores, and be accountable for quality, reliability and delivery.
• Convert platform strategy and architecture direction into roadmaps, quarterly plans and finished outcomes. Work with architects and engineering leaders, and adjust focus across data, knowledge and AI work as priorities shift.
• Treat the platform as a product for internal teams, including domain delivery teams, BI, context and semantic teams, and AI engineering. Set service levels, onboarding paths and standard paved roads so teams can self-serve and build AI-native systems quickly and safely.
• Build agentic operations that automate routine team work such as pipeline monitoring, failure triage, data quality checks and remediation. Create a reusable framework and standards that other teams can adopt for their own operations.

Requirements

• Dive deep into architecture discussions, clear blockers for team members and judge effort estimates well, while enabling senior and principal engineers to lead system design.
• Understand the full engineering lifecycle, from enterprise data ingestion and transformation through knowledge retrieval and context engineering to deploying production agents.
• Drive architectural strategy and also work through implementation details, making practical choices that reduce technical debt.
• Lead people well: hire, mentor and advance engineers, and build a culture of ownership and engineering excellence.
• Work across data platforms, knowledge engineering and agentic operations. Lead a globally distributed team with an ownership mindset, a bias for action, and comfort in a fast-changing AI field.
• Practical understanding of generative AI architectures, especially RAG and enterprise knowledge systems, with experience building 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 proven technical and people leadership.
• Strong hands-on skill in Python and SQL, with a solid base in modern data engineering and distributed systems.
• Experience designing and running production-grade data platforms on AWS or another major cloud, using tools such as Snowflake, Databricks or BigQuery, along with dbt.
• Strong grounding in data quality, metadata, lineage, governance and observability, with a record of turning unclear problems into scalable engineering solutions.
• Preferred: production experience with RAG or GraphRAG platforms, knowledge graphs and graph databases (ArangoDB, Neo4j, Amazon Neptune), vector databases, embeddings, hybrid retrieval and re-ranking.
• Preferred: hands-on experience building infrastructure for AI agents and agentic workflows with LangGraph, LangChain or LlamaIndex, plus LLM evaluation, AI observability and LLMOps.
• Preferred: deep cloud-native platform engineering across Kubernetes, Terraform and Ray/Ray Data, with a record of building internal developer platforms and reusable data and AI platform services.

Conditions

Hybrid role: 3 days per week in the office, based in Santa Clara, CA.

Full-time position.

Base pay range is $164,000 - $205,000 USD. This covers the target range for new hires across all US locations and may be higher or lower depending on skills, experience and relevant education or training. The range excludes commission, bonus, equity (if applicable) and benefits.

The company follows applicable pay transparency rules and provides reasonable accommodations in its hiring process for candidates who need them.

Applying means following applicable laws, regulations and company policies, including those on security and privacy.

Benefits

• Health plan options.
• Vacation and sick time plans.
• Parental leave options.
• Retirement options.
• Education reimbursement.
• In-office perks.