Manager, IT Enterprise Engineering

Postman•San Francisco, CA

3d•$250,000 - $290,000•Onsite

About The Position

This is a technical leadership role at the center of Postman's IT infrastructure and its future. You'll own the strategy and execution for IAM, MDM SaaS platform engineering, infrastructure automation, and the emerging AI/agent layer that will reshape how IT operates. You're the technical pillar lead for Enterprise Engineering, not just a people manager. That means you're accountable for architecture decisions, integration quality, and the operational reliability of every system your team touches. But beyond keeping today's platforms running, you're also responsible for positioning Postman's IT infrastructure for an AI-native future, building the foundations that AI agents and LLM-powered workflows will operate on. You'll inherit a strong group of engineers and be expected to raise the ceiling on what they can deliver, both with traditional tooling and with the next generation of intelligent automation.

Requirements

7+ years in IT engineering or platform engineering, with at least two to three years leading a technical team.
Deep hands-on experience with Okta (or equivalent IdP), SSO/SAML/SCIM, and lifecycle automation.
Strong background in SaaS administration and integration at scale. You've managed complex multi-tool environments and understand the challenges that come with them.
Experience with MDM platforms (Jamf preferred), endpoint management, and Apple ecosystem at scale.
Demonstrated ability to build and maintain automation (scripting, APIs, workflow orchestration), not just manage people who do.
Genuine curiosity and hands-on engagement with AI/LLM technologies. You've experimented with or deployed AI-driven automation, not just read about it. You understand the potential and the limitations.
An architectural mindset for how AI agents interact with enterprise systems, including identity, permissions, data access, and auditability. You think about the plumbing, not just the chatbot.
Comfort operating in a fast-paced, high-growth environment where priorities shift and you need to adjust without losing the thread.
Clear, direct communication style. You can explain technical decisions to non-technical stakeholders without dumbing things down.

Nice To Haves

Experience deploying AI agents or LLM-based automation in an IT or enterprise operations context, such as support bots, autonomous workflow agents, or intelligent triage systems.
Familiarity with AI agent frameworks, orchestration patterns, and the emerging tooling ecosystem (e.g., function calling, RAG architectures, agent-to-agent communication).
Experience with Workday integrations or HRIS-to-IT automation pipelines.
Familiarity with zero-trust architectures (ZTNA, SASE) and how identity fits into that model.
Background in M&A IT integration, including folding acquired teams and tech stacks into an existing ecosystem.

Responsibilities

Lead and grow a team of systems and automation engineers focused on IAM (Okta), SaaS lifecycle management, infrastructure automation, and AI-enabled operations.
Define and own the technical roadmap for the Enterprise Engineering pillar, aligning it with company-wide IT strategy, security requirements, and the evolving AI/agent landscape.
Architect and oversee high-stakes integrations (HRIS, identity, MDM, SaaS platforms) with an emphasis on reliability, scalability, and API-first design that enables AI agent interoperability.
Lead the evaluation, design, and implementation of AI and AI agent capabilities within IT, spanning LLM-powered support and triage through to autonomous workflow execution.
Build the data, integration, and governance foundations that AI agents need to operate safely and effectively within IT systems. These foundations must be identity-aware, auditable, and secure by default.
Partner closely with Security, DevOps, and People teams to ensure identity and access management is airtight and audit-ready.
Build repeatable, automated workflows that reduce manual toil and let the team focus on engineering problems rather than ticket queues, increasingly leveraging AI to handle what automation scripts can't.
Serve as a technical escalation point for complex infrastructure and integration issues.
Manage vendor relationships and evaluate new tooling.
Establish engineering standards, documentation practices, and runbooks that make the team's work transparent and maintainable.