Hybrid Staff Technical Program Manager, Infrastructure FinOps

at Zigsaw

• Job title: Staff Technical Program Manager, Infrastructure FinOps
• Job type: Full Time
• Experience level: Lead
• Salary: $145,747 - $300,067 per year
• Degree requirement: No Education Requirement
• Tech skills: Cloud
• Location requirements: Hybrid, San Francisco, United States

About the role

• Staff Technical Program Manager overseeing infrastructure governance and financial models at Pinterest. Collaborating with Product and Engineering to execute multi-quarter programs with measurable outcomes.

Responsibilities

• Partner with Product and Engineering to translate infrastructure strategy into executable multi-quarter programs, with clear scope, sequencing, dependencies, governance forums, and measurable outcomes across the Infra Governance portfolio.
• Lead capacity planning as a core operating motion: connect product demand, infrastructure supply, growth assumptions, and technical constraints into a durable planning cadence that enables earlier, better investment decisions.
• Own budgeting, forecasting, and variance analysis for infrastructure governance programs, building clear visibility into where investment is going today, where it is expected to go next, and what is driving movement versus plan.
• Develop financial models, cost models, and scenario-planning frameworks that translate technical choices into business impact and support explicit trade-off discussions across capacity, cost, reliability, performance, and speed.
• Build an investment transparency matrix for infrastructure and AI spend, including ownership, allocation, utilization, forecast, actuals, and decision points, so leadership can quickly understand where resources are being consumed and where intervention is needed.
• Establish governance for AI token utilization and allocation, including forecasting demand, tracking usage, improving transparency, and helping teams understand the financial implications of model and token consumption choices.
• Operationalize durable mechanisms for budgeting reviews, forecast updates, variance investigations, anomaly management, and optimization follow-through in partnership with Product, Engineering, Finance, Security, and Data.
• Lead trade-off discussions that improve decision quality: synthesize technical inputs, business priorities, and financial signals into clear options, recommendations, and escalation-ready decisions.
• Build the governance layer for execution: run portfolio reviews, dependency management, roadmap health reviews, planning checkpoints, and executive updates that keep large cross-functional programs aligned and on track.
• Identify high-leverage opportunities to automate operational toil, especially in recurring workflows like status synthesis, planning reviews, dependency tracking, intake triage, forecast reconciliation, and action/decision capture.
• Build AI-assisted frameworks, lightweight models, and workflow automations that improve decision-making, including dashboards, scenario tools, cost-model templates, token-usage views, and planning helpers that increase signal while reducing manual overhead.
• Use GenAI as the default operating model for EP PgM execution—producing AI-assisted first drafts of core program artifacts, modernizing high-toil workflows into AI-first mechanisms (e.g., intake triage, status synthesis, action/decision extraction, risk & dependency tracking), and synthesizing signals to proactively surface risks, decision/trade-offs, and escalation paths.
• Prototype solutions to augment decisions through data (e.g. dashboards, data analysis) or simplify processes (e.g. process and workflow helpers, or internal tools) using AI coding assistants (“vibe coding”).
• Follow Pinterest AI guidance for risk, governance, and safety-by-design: appropriately handle sensitive data, validate AI-generated outputs, document assumptions/limits, and ensure AI-assisted workflows meet applicable policy/compliance expectations before broad adoption.

Requirements

• 8+ years of Technical leadership experience owning large, ambiguous, cross-functional programs with senior stakeholder visibility and durable business impact.
• Strong experience in infrastructure, platform, cloud economics, or adjacent environments where capacity, cost, performance be managed together.
• Proven ability to translate strategy into executable programs: turning broad technical goals into roadmaps, governance mechanisms, planning cadences, and measurable outcomes.
• Demonstrated strength in capacity planning, budgeting, forecasting, variation planning in environments with meaningful scale and complexity.
• Strong financial modeling and cost modeling skills: able to build simple, credible models, pressure-test assumptions, and use data to guide prioritization and trade-off discussions.
• Experience building transparency mechanisms for investment and utilization, including understanding where infrastructure or AI spend is going, where it is expected to go, and how ownership and allocation should work.
• Strong technical breadth and judgment: able to partner effectively with engineering teams, drive clarity around requirements, and facilitate technical trade-offs without relying on coding depth; this is directly aligned to the technical breadth and judgment expectations in the interview guidance.
• Excellent cross-functional collaboration and executive communication: able to align teams with conflicting priorities, communicate clearly upward, and influence without direct authority; this is directly aligned to the XFN collaboration and communication expectations in the interview loop.
• Experience building durable mechanisms rather than just managing tasks: governance processes, planning systems, dashboards, review cadences, and decision frameworks that scale across teams.
• Workflow design, AI fluency, data & insights orientation: experience turning repeatable program work into durable, low-toil mechanisms and improving decision-making by using GenAI (e.g., strong prompting, vibe coding lightweight scripts/tools, dashboards, data analysis and leveraging agents where appropriate)
• Safety-by-design AI fluency: experience operating within AI governance expectations (risk assessment, data handling, model/output validation, auditability/traceability) and proactively identifying where AI use is not appropriate or requires additional controls.