GTM Strategy Manager

The Role

Reporting to the GTM Planning Lead, the Central Planning Manager is responsible for translating GTM strategy into headcount capacity plans, quotas, and pipeline-generation targets that drive the right outcomes across direct and partner motions. You will be the hands-on owner of the planning models and a trusted partner to GTM and Finance leadership, balancing competitiveness, simplicity, scalability, and financial discipline.

You will lead headcount capacity planning and overall target design (booking, consumption, pipegen), and partner with finance, field ops and GTM leadership to align headcount, top level targets and compute supply.Your work will directly influence revenue predictability, seller behavior, retention, and long-term growth.

This is not a pure admin seat and not a blank-slate build. You inherit working capacity and target models and extend them as Fireworks scales.

Consumption and hybrid commit + usage economics are core to how we sell and how our business grows. The right candidate has intimate knowledge of business models that rely on a usage-based or hybrid commercial model — not only license ARR — and can design capacity plans that meet our strategic objectives.

Responsibilities

GTM Planning, Capacity & Quotas

• Drive the annual Go-To-Market planning process for the COO organization under the GTM Planning Lead.

• Owning GTM capacity planning and target-setting, translating growth targets into coverage, headcount, and productivity plans across Sales, Field Engineering and CS.

• Applying and refining target-setting methodologies, ensuring targets are grounded in market opportunity, historical performance, and productivity expectations.

• Partnering with Finance, Field Ops, and GTM leadership to build and maintain models of capacity needs, ramp assumptions, productivity, and hiring requirements for annual and long-range planning, including scenario models that show the tradeoffs between headcount, pipeline, compute supply, productivity, and revenue before decisions are made, and present scenarios to the GTM Planning Lead.

• Maintaining planning frameworks and analytics to monitor capacity, quota attainment, and productivity and inform in-year adjustments.

• Own pipeline-generation (pipegen) targets by segment and team, and track pipeline coverage against them.

• Partner with the GPU management team so headcount, pipeline, and consumption assumptions feed the GPU demand forecast.

Systems, Forecasting & Automation

• Build and own a native end-to-end process and tooling for automation of business plan modeling.

• Partner with Data Engineering and GTM Engineering on source-of-truth data (CRM, billing/usage, roster/quotas) so calculations are reproducible and auditable.

Qualifications

• 6+ years in GTM planning, quota setting, and/or productivity-related revenue operations workstreams

• Strong understanding of go-to-market design and sales planning, including how capacity, quota, and pipeline targets fit together

• Significant experience presenting analysis to senior leaders and closely partnering with leaders at the Director and VP level

• Advanced analytical and financial modeling skills with a superb ability to drive high value insights

• Hands-on experience building headcount, ramp, and quota capacity models for a consumption-based or hybrid commit-plus-usage business

• SQL and BI fluency, and experience with a planning or forecasting platform (for example Anaplan, Clari or Xactly)

• BA/BS degree in engineering, accounting, finance, economics or other quantitative fields preferred

Preferred

• Experience at an AI/ML infrastructure, developer tools, or API-first company

• Familiarity with manager overrides, overlay roles (e.g. SAs / field engineers), marketplace/channel nuances, and self-serve → sales-assisted conversions

• Experience planning through a period of hypergrowth

• Background in investment banking, finance or management consulting

Why Fireworks?

• Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving.