Principal Product Manager- Platform Infrastructure

Translate platform investments into business outcomes (reliability, cost, developer velocity, and scale) for engineering and product leadership. Someone who is energized by hard technical problems, makes decisions with incomplete information, and earns credibility with engineers by understanding the system, not by deferring to it. This is not a role for a PM who waits for engineering to tell them what's possible. You should be able to walk into an architecture review, hold your own, and walk out having shaped the direction. We recognize that background takes many forms. We will consider candidates who meet one of the following experience profiles: You've built something from scratch, owned the full technical and product surface, and made high stakes decisions without a safety net. That experience, the judgment, the urgency, and the firsthand understanding of what it means to ship, is exactly what this role demands. Strong: Engineering background transitioning into product. 5+ years of software engineering experience in distributed systems, platform, or infrastructure, combined with 5+ years of product management in a growth startup or enterprise SaaS environment. You speak engineering fluently because you were one, and you bring that credibility into every roadmap conversation. Qualified: Senior PM with deep platform focus. 10+ years of product management in enterprise SaaS or a high growth startup, with at least 5 of those years focused on technical platform, infrastructure, or developer tools. You've earned technical credibility through proximity and rigor, and you've demonstrated you can hold your own with principal engineers on architecture decisions. Regardless of path, you will need to demonstrate the following on day one: Distributed systems depth. Asynchronous processing, job orchestration frameworks, multithreaded execution, message queues, and background task scheduling at scale. The Jobs Framework is the backbone of Ready's background processing; you need to understand what it's doing and why. POD based multitenant architecture fluency. This team owns cross POD data synchronization and communication. Tenant isolation, data partitioning, and fairness patterns are not concepts to learn here; they're context you bring. Containerization, microservices, horizontal scaling, and cloud migration strategy. Driving a GKE or equivalent migration is a near term deliverable. Redis, Memcached, cache invalidation, cache stampede prevention, distributed caching, and the ability to trace a latency problem through the stack. IaC and DevOps literacy. Terraform, configuration management, infrastructure automation, and CI/CD practices at the platform level. The ability to align engineering, architecture, security, DevX, and product teams across a globally distributed organization. Data driven prioritization. System metrics (latency, throughput, error rates, SLOs/SLIs), performance benchmarking, and operational dashboards drive your roadmap decisions. Executive communication. You can translate a cache invalidation strategy into a business outcome conversation with a VP in two sentences. You actively use AI agents in your daily PM workflow: PRD drafting, technical research, document synthesis, metrics analysis, and stakeholder communications. You've worked in an environment where engineers build with AI coding assistants and you understand how that changes how requirements are written and how teams operate. Candidates who treat AI as an occasional tool rather than a core work practice will not be competitive for this role. Familiarity with Quartz, Hangfire, Apache Airflow, Celery, or comparable job scheduling systems. Experience with blob storage systems and data lifecycle management at scale. Grafana, Datadog, New Relic, OpenTelemetry, and establishing SLOs/SLIs for platform services. Predictive scaling strategies, schedule based scaling, and resource utilization optimization. Knowledge of HCM/HRIS integration patterns and enterprise HR data flow architecture. Vulnerability management lifecycle, security scanning toolchain integration, penetration testing response, and secure SDLC. Advanced degree (MBA, Computer Science, or related field) or equivalent practical experience. What Success Looks Like In the first 90 days: Deliver GKE migration foundations and containerization phase 1 scope definition with clear sequencing, dependencies, and success criteria. Establish baseline SLO targets for Jobs Framework processing latency and cache hit rates across Platform Services. Complete scope alignment with the Integration Framework team on Audit Framework ownership boundaries. Demonstrate an AI native working rhythm: use agentic tools to drive your own onboarding, synthesize technical documentation, and produce your first roadmap artifacts. In FY27: Technical credibility earned. Engineers and architects bring you into hard conversations before decisions are made, not after. You've made defensible architectural tradeoff calls on platform scope, navigated competing approaches with data, and earned a genuine seat at the table where infrastructure direction gets set. Complex migration navigated. You've driven a high stakes infrastructure initiative involving sequencing, dependency management, and cross team coordination without disrupting active workloads. The engineering team executed against a plan you owned, and the outcomes were measurable. Platform reliability owned, not monitored. SLOs are established, visible, and trending in the right direction. You built the accountability model, not just the dashboard. When incidents happen, recovery is faster because of decisions you drove. Developer productivity moved. Domain teams build faster because of investments you prioritized. You identified the friction points slowing internal developers down, translated them into platform requirements, and measured the improvement. Internal customers notice the difference. Security managed proactively. Security findings don't age in your backlog. You've built a working relationship with security teams, established a prioritization model the engineering team trusts, and maintained compliance without it becoming a crisis each cycle. Commercial instincts demonstrated. Infrastructure costs are a lever you actively manage, not a byproduct you report on. You can connect a caching architecture decision to a business outcome in two sentences and defend it to a VP. The platform meets the demands of AI workloads, not just human traffic. You've had the hard SLO conversations with the AI Framework team, understand what their workloads require, and ensured Platform Services is designed for both. Audit trail systems, SOC2/GDPR/HIPAA requirements, data retention, and vulnerability management lifecycle. Builder's decision making style. You make calls, you defend them, and you update when the data changes. You don't produce requirements documents that hedge every decision back to engineering.