Platform Automation Engineer Jobs in Phoenix, AZ

Role Responsibilities:

Own it: strong product execution across the lifecycle

• Ownthe roadmap and backlog. Sequence workbasedon customer impact, technical dependencies, and business priorities.

• Define what "done" looks like andhold toit.Regardless of whether the work is an early incubation, a platform capability driving active migration, or a mature product managing adoption at scale. The phase changes; the ownership standard does not.

• Get it in front of real customers or internal consumers early and often. Run structured discovery with developers, engineering teams, and ISVs to surface what is not working,validatewhat is, and generate the evidence that drives the roadmap.

• Make hard scope calls. Own the triage of what ships this quarter, what is a future bet, and what is out. Make those calls transparently, document the reasoning, and keep momentum.

• Drive velocity while holding the quality bar. Your customers - and the engineering teams that depend on your platform - need to defend their work downstream. Platformbugscompound. Hold the shipping cadence and the reliability standard together.

• Know when you are managing discovery versus managing adoption. Launching a net-new capability requires different instincts than scaling an existing one from 10% to 50% of customers. Apply the right model for where the product is, not where you wish it were.

Build the right thing: platform-first product thinking

• Define what the capability means for the developers andend users who depend on it. Platform PMs own things others build on - a wrong decision in versioning, access control, or API design compounds across dozens of teams. Build for long-term stability, not just the next release.

• Design for the human-in-the-loop. Whether in AI-driven workflows, permission management, or automation pipelines - the right escalation path, governance signal, and audit trail make operators more capable.Build forthat model deliberately.

• Treat trust as a product foundation. Security, auditability, and platform governance are themoat. Partner with engineering and security to keep defensibility a first-class design constraint from day one.

• Use AI as a force multiplier. Use AI capabilities - in prototyping, research synthesis, spec drafting, and customer insight - to move faster. Model this for the team and share your innovations with the rest of the organizations becausewe'erall learning and in this together!

Contribute to the right ecosystem

• Define the platform patterns that scale. Working with engineering and architecture, apply the contracts and governance boundaries that let internal and external builders develop on a stable, well-governed surface. Versioning, security, capability scoping, and backward compatibility are product decisions that compound.

• Translate the platform to its builders. Turn technical concepts - cloud-native migration patterns, API security models, AI extensibility standards - into narratives that resonate with developers, domain experts, and buyers.

• Develop customer-facing documentation, enablement guides, and release notes for platform capabilities. Capture best practices from early adopters and package them into reusable assets that accelerate adoption.

Lead through influence: cross-functional and data-driven

• Be the connective tissue across product, engineering, and go-to-market for your area. Hold the picture across the development experience, runtime governance, distribution, and customer value. Pull the right people together, keep them aligned, and clear the blockers.

• Track the right metrics. Adoption, migration progress, platform reliability, and time to first successful integration. Define the leading indicators before the lagging ones catch up.

• Communicate progress and risks clearly. Report what you know, what you do not, and what you need - to your leadership, your engineering partners, and your customers.

Minimum qualifications

• 7+ years in software product management, withdemonstratedownership across more than one product lifecycle stage - incubation, active migration, or scaled adoption.

• Demonstrated ability to deliver through ambiguity: you have defined scope under uncertainty, made hard prioritization calls, and shipped with customers in hand.

• Experience with developer-facing or platform-facing products - APIs, SDKs, extension frameworks, security primitives, or cloud infrastructure - with measurable adoption.

• Hands-on experience with cloud platform architecture (Azure preferred) and comfort in technical discussions around distributed systems, platform APIs, and cloud-native design. Working knowledge of AI-enabled platform patterns - developer tooling, agentic workflows, or AI-native services - and where reliability and governance constraints bind.

• Solid understanding of the software development lifecycle and modern delivery practices, including experience in AI-powered SDLC environments where agentic tooling is part of how the work gets done.

• Data-driven: you use data to set goals, size bets,monitorproduct health, and changecourse.

• Strongcommunicatoracross technical and non-technical audiences. You translate complex platform and infrastructure concepts into clear customer and business outcomes.

• Active user of AI tools to prototype, synthesize research, draft specs, and move faster. This is how the work gets done here.

• Comfortable working alongside or mentoring junior product managers -you'vecontributed to someone's growth as a PM, even without a formal reporting relationship.

Preferred qualifications

• Background in legal technology, eDiscovery, compliance, or another domain defined by complex, high-stakes workflows where defensibility, reproducibility, and audit trails are first-class requirements.

• Experience with cloud-native platform migrations: moving workloads off legacy frameworks onto moderncompute, storage, and auth primitives.

• Familiarity with emerging AI integration standards such as Model Context Protocol (MCP) or Agent-to-Agent (A2A) and how they shape platform extensibility.

• Experience building on or contributing to developer ecosystems, partner integrations, or extensibility platforms at enterprise SaaS scale.

• A technical foundation (computer science, engineering, data science, or equivalent) that lets you work credibly with senior engineers.