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GTM Engineering Manager

AI Summary
Tremendous is hiring a GTM Engineering Manager to lead and grow a hands-on team building revenue-operations systems across marketing, sales, and customer success. The role combines people management with direct technical work on AI agents, workflow automation, APIs, integrations, and internal tooling. This manager will establish engineering standards for version control, reviews, documentation, ownership, and rollback practices while partnering with GTM planners on delivery priorities. Success requires deep GTM-funnel knowledge, strong written communication, and the ability to operate autonomously in a documentation-first remote environment.
Remote from
USA
Salary
USD 190k–230k / yr
Employment
Full Time
Experience
Senior
Published
Apply before
6 Dec 2026
Listing views
12
Application actions
1
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Opportunity details

About this role.

Tremendous is the global platform built for businesses to send thousands of payouts to anyone, anywhere, for free. We’re trusted by 20,000 leading organizations like Atlassian, MIT, and United Way to deliver gift cards and money to millions of recipients worldwide.

In both our product and our workplace, we’re intentional about making work more efficient, flexible, and fulfilling. Tremendous is a fully remote, high-documentation, low-meeting culture, which means more time for what matters in both your professional and personal life.

Our customers, who include researchers, marketers, HR teams, and nonprofits, rave about how fast and easy it is to use Tremendous. Check out our ratings on G2.

Tremendous is profitable and growing without outside investors.

About the role

We’re looking for a GTM Engineering Manager to lead our team of GTM engineers. Our Revenue Operations team works like a product and engineering org: planners define what marketing, sales, and customer success need, and GTM engineers design and build it with LLMs, workflow tools, and code. You’ll own how that team works: its standards, its infrastructure, and the growth of the people on it. You’ll stay hands-on, building the systems that make the whole team faster.

What you’ll do

  • Manage and develop a team of GTM engineers across marketing, sales, and platform work. Hire the next ones.

  • Set the team’s engineering practices: version control, review, documentation, and ownership for every workflow, agent, and integration in production.

  • Build the team’s own infrastructure, including the agents and tooling that keep work assigned, tracked, and current.

  • Partner with GTM planners on the roadmap: bring the build-side view on what’s feasible and how to sequence it, and put the right builder on the right project.

  • Act as the team’s ambassador to our AI enablement engineering team, and shape how work moves from a GTM engineering prototype to long-term engineering ownership.

  • Own relationships with the technical vendors the team builds on, such as Cargo.

What you’ll bring

  • 7+ years across RevOps, GTM engineering, marketing or sales ops engineering, or internal tools, with real hands-on building.

  • 2+ years managing builders or technical roles, with people you’ve grown into stronger roles.

  • You’re a capable builder yourself: AI agents, workflow tools (Cargo, Clay, n8n, Zapier, or similar), APIs, webhooks, and Git.

  • Engineering-manager instincts: you’ve set up review, documentation, and rollback practices on systems other people depend on.

  • Fluency across the GTM funnel: marketing, sales, and customer success.

  • Strong written communication. We’re a documentation-first culture.

  • Autonomous and self-directed. Tremendous is not a great fit for people who default to waiting for instructions.

  • High empathy. You care about your teammates and the people who use what your team builds.

Nice to haves

  • HubSpot depth, especially workflows and APIs

  • Cursor, Claude, or similar AI-assisted development as a daily habit

  • SQL and Snowflake

  • Experience standing up a GTM engineering or RevOps engineering team from a few people

  • Usage-based or fintech GTM experience

What’s cool about the role

  • The team already exists and is shipping. You’re making a strong team faster, not starting from scratch.

  • The company is AI-forward. Cursor daily, regular AI tool evaluations, leadership fully bought in.

  • Profitable and growing without outside investors.

  • Competitive pay, equity, and benefits. Base salary: $190,000-$230,000

  • Fully remote. This role is US Remote.

  • Smart people and a great culture. See our company handbook.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior technical leadership role requiring both proven people management and hands-on expertise in AI-enabled GTM automation. The manager must create durable operating standards while coordinating complex work across multiple revenue functions and engineering stakeholders.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$210,000
US market range (per year)$180k–$250k
AI insightThe employer explicitly lists a yearly USD base-salary range of $190,000–$230,000, with a midpoint of $210,000. This is a credible range for a US-based senior engineering manager leading GTM and RevOps automation work; the estimated US market range is $180,000–$250,000 per year, excluding equity and benefits.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How have you balanced people management with remaining hands-on in technical systems?

I set clear ownership boundaries so team members lead their systems while I stay engaged on architecture, high-risk integrations, and reusable platform capabilities. I protect regular time for coaching and technical review, using my hands-on work to unblock the team rather than becoming a bottleneck.

How would you establish reliable engineering practices for a GTM engineering team?

I would start with lightweight but mandatory standards for Git-based version control, peer review, documentation, monitoring, rollback plans, and named owners for every production workflow. I would then introduce templates and automated checks so reliable delivery becomes the easiest path rather than an administrative burden.

Describe your approach to prioritizing requests from marketing, sales, and customer success.

I evaluate requests against expected business impact, urgency, technical risk, reuse potential, and operational cost. I partner with planners to make trade-offs explicit, sequence foundational work before one-off requests when appropriate, and communicate a visible roadmap with clear ownership and delivery milestones.

What risks do you consider when deploying AI agents and workflow automations in revenue operations?

I consider data access, prompt and workflow reliability, duplicate or incorrect customer actions, observability, approval gates, and fallback procedures. I use controlled rollouts, audit logs, measurable success criteria, and human review for high-impact actions before expanding automation.

How would you help a GTM engineering prototype transition to long-term engineering ownership?

I would define an ownership handoff that includes documented architecture, interfaces, security and reliability expectations, test coverage where applicable, operational runbooks, and a clear maintenance owner. Early alignment with the receiving engineering team helps ensure prototypes are built with production constraints in mind.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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