Technical Program Manager at MNTN in Location not listed

MNTN is looking for a Technical Program Manager to own products that help our Revenue and Operations teams plan, prioritize, and act more effectively. At MNTN, Technical Program Managers operate as product managers: they understand user problems, set product direction, and own outcomes, not simply delivery plans and coordination.

This role will own internal products related to business forecasting, pipeline management, lead enrichment and automation, and the productization of customer-intelligence signals. You’ll turn data and insights produced across MNTN into useful tools and workflows that help teams make timely, consistent decisions at scale.

You’ll own these products end to end—from discovery and prioritization through delivery, adoption, and iteration. The work includes custom-built products and integrations across SaaS systems, but does not include IT administration or broad business-process ownership.

Reporting to the Group Technical Program Manager for Internal Products, you’ll work closely with Engineering, Data, Revenue Operations, Sales, Growth, and Finance. You’ll operate within the broader Internal Products strategy while independently owning the roadmap and outcomes for your product area.

What you'll do

• Own the roadmap and product lifecycle for MNTN’s forecasting, pipeline management, lead enrichment, automation, and related customer-intelligence products.
• Build trusted forecasting and pipeline products that give Revenue and Operations teams clearer visibility and help them make better planning and prioritization decisions.
• Translate customer-intelligence signals produced by other teams into scalable tools and workflows that help users take timely, consistent action.
• Identify and prioritize opportunities to replace fragmented data, spreadsheets, manual analysis, and repetitive handoffs with durable product capabilities.
• Work directly with Revenue, Operations, Sales, Growth, and Finance users to understand their workflows, validate problems, and distinguish product opportunities from requests better addressed through process or existing tools.
• Define requirements and make product tradeoffs based on user value, business impact, data readiness, technical feasibility, and ongoing maintenance cost.
• Partner with Engineering and Data on data models, system integrations, product architecture, reliability, and the interpretation of data across multiple sources.
• Drive adoption after launch by measuring usage, gathering feedback, addressing gaps in trust or usability, and iterating based on demonstrated value.
• Communicate roadmap priorities, decisions, dependencies, and results clearly to the Group TPM and cross-functional partners.

What success looks like

• Revenue and Operations teams rely on trusted forecasting and pipeline products to guide planning and day-to-day decisions.
• The products have sustained, measurable adoption among their intended users.
• Customer-intelligence signals are translated into scalable tools and workflows that enable timely, consistent action without one-off analysis or manual intervention.
• High-value workflows require less manual effort, fewer handoffs, and less reconciliation across disconnected systems or data sources.
• Products have clear success measures and demonstrate improvements in efficiency, decision quality, or operational capacity.
• The product area has a focused roadmap that supports the broader Internal Products strategy and responds to evidence rather than disconnected stakeholder requests.

What you'll bring

• 3+ years of experience in product roles focused on workflow automation or related internal products.
• Direct experience in at least one of the following areas: revenue operations, business forecasting, pipeline management, or customer-intelligence products.
• A track record of owning products end to end, from problem discovery and prioritization through launch, adoption, and iteration.
• Experience building products for internal users and driving adoption across teams with different workflows, incentives, and levels of technical fluency.
• Strong data fluency and the ability to turn fragmented data and analytical outputs into clear, useful product experiences.
• Technical fluency with APIs, system integrations, data models, data pipelines, and the tradeoffs involved in connecting multiple platforms.
• The ability to productize predictive signals or analytical insights without needing to own the underlying models.
• Strong judgment about when to build custom capabilities, integrate existing systems, or solve a problem without creating another product.
• Clear, direct communication and the ability to work effectively with engineers, data teams, operational users, and business leaders.