GTM Engineer

About Straddle, Inc.

Straddle is an open-banking payments platform for account-to-account money movement. We combine identity verification, fraud detection, and multi-rail payments, ACH, RTP, and soon FedNow, into a single API, so businesses can move money between bank accounts as easily as sending a text.

We're headquartered in Broomfield, Colorado, building infrastructure for how modern businesses get paid. Teams are lean, decisions are quick, and layers are minimal. If you care about quality and velocity in equal measure, you'll be in good company.

About the role

The GTM Engineer builds the measurement and conversion machinery underneath Straddle's commercial motion. The sequencing here is deliberate, and it is the opposite of how this role is often scoped: instrumentation first, outbound later.

Buyers are already finding us. The immediate opportunity is to make that discovery path clearer and more measurable, not to bolt an outbound machine onto a company that cannot yet tell which channel produced which dollar. So your first quarters are about capture and evidence: standardized CRM stages and required fields across the direct, platform, bank, and technology-partner motions; instrumented sandbox behavior and activation milestones; distinct conversion paths for each buyer type instead of one generic demo funnel; and baseline funnel, retention, and cohort metrics that make every later decision arguable from data.

You own the numbers as well as the plumbing. The KPIs and dashboards the commercial team runs on are yours to define, build, and keep honest, and the CRM has to stay the system of record that every other tool defers to rather than one more place data disagrees with itself.

That work has teeth because the metrics are unforgiving. A sandbox registration is not pipeline. Contract-to-activation rate matters more than contract count. A strong channel can look weak because customers get stuck in onboarding, and a flood of sandbox signups can look excellent while producing almost no activated revenue. Building systems that tell those cases apart is the job.

Attribution is genuinely hard here and we do not pretend otherwise. Infrastructure purchases run long, involve technical, compliance, financial, and executive stakeholders, and rarely reduce to one touch. Last-touch attribution would actively mislead us. You build something better: source of first contact, original landing page, content consumed, events attended, sandbox activity, sales interactions, strategic introductions, and contracted versus activated revenue by source.

Outbound comes later, and only once segmentation, CRM discipline, and conversion measurement are real. When it does, it is narrow and evidence-based - accounts where we have a specific reason to believe there is a fit, not broad-volume prospecting. AI handles account research, signal collection, enrichment, personalized drafting, sequencing, and follow-up. A human sends. The purpose of automation here is relevance and consistency, not volume, and generic AI spam would cost us more credibility than it could ever generate. Where a platform's terms prohibit automated messaging, we comply - no clever workarounds, at any volume, because the accounts at risk are the founder's and the CBO's own networks.

You report to the CBO. This is a build role at a company with no existing GTM infrastructure, so most of what you ship is the first version of something.

What you'll do

• Build the data spine. Connect CRM, product and sandbox telemetry, and revenue so a single account is followable from first touch to activated dollar. Everything else depends on this.
• Integrate the stack around the system of record. Connect the CRM to enrichment, product telemetry, billing, support, and marketing tooling, and keep the CRM authoritative. Own the sync logic, the field mapping, and the failure modes, so no one has to ask which system is right.
• Standardize the funnel. Implement the funnel stages and required fields, kept separate across the direct, platform, bank, and technology-partner motions so no motion's performance hides inside another's.
• Instrument sandbox and activation. Make meaningful integration events, time-to-sandbox, time-to-production, time-to-first-transaction, and activation rate visible without anyone having to ask.
• Build honest attribution. Multi-touch, multi-stakeholder, and explicit about its own limits. Report contracted and activated revenue by source separately.
• Own the KPIs and the dashboards. Define and build the reporting the company runs on: funnel stage conversion, activation, cycle length, win and loss reasons, and pipeline by motion and by conversion path. Carry unit economics in the same system, kept separate for direct, platform, bank, strategic-partner, referral, organic, event, and outbound sources, with partner commissions visible rather than buried in cost of goods sold. A dashboard nobody trusts is worse than no dashboard.
• Build the ICP and signal framework. Turn the qualitative ICP into something operational: a scored, testable definition of fit per motion, backed by observable buying signals - card payments without bank payments, public payment-modernization discussion, hiring payment infrastructure engineers, disconnected vendor stacks, visible ACH or open banking limitations, customers of strategic partners. Then validate it against what actually closed and activated, and correct it. The goal is predictable outcomes from repeatable inputs, not a longer list.
• Build outbound when the evidence supports it. Account-based and outbound programs wait until segmentation, CRM discipline, and conversion measurement are real - then they run narrow and account-specific, AI-assisted for research and drafting and human-sent. Nothing that violates a platform's terms of service, regardless of what a vendor promises.
• Automate the manual work away. Anything done twice in a commercial workflow is a candidate. Write real code when a no-code tool will not hold up.

Qualifications

Required Qualifications

• GTM systems engineering. 3+ years in GTM engineering, revenue operations, growth engineering, or marketing engineering - or a software engineer who has owned commercial systems end to end.
• You write real code. Python, TypeScript, and SQL, not only a no-code stack. You can build a service, script an enrichment pipeline, and query a warehouse without waiting on anyone.
• CRM architecture and data quality. You have designed object models, stage definitions, and required-field discipline from scratch, and handled enrichment, deduplication, and identity resolution across messy commercial data. You know how fast bad data destroys trust in a system.
• Integration engineering. You have made systems talk to each other in production - APIs, webhooks, sync patterns, conflict resolution - and kept one of them authoritative rather than letting every tool hold its own version of the truth.
• Reporting people actually use. You have built dashboards and KPI definitions that a commercial team ran on, and defended the numbers when someone disliked them.
• Measurement before growth. You have built instrumentation and attribution first and resisted pressure to launch campaigns on top of numbers you knew were wrong. This instinct is the core of the role.
• Compliance judgment. You treat platform terms of service as real constraints and would rather ship a slower compliant system than a faster one that risks the company's accounts. We are a payments company; this is not optional.
• Communicates with commercial people. You explain systems and their limits plainly, and you push back with evidence when a request would produce misleading numbers.
• AI-native. You build AI into the workflows themselves - research, enrichment, brief generation, drafting - and you have shipped something that worked this way.

Preferred Qualifications

• Product analytics and telemetry. Instrumenting sandbox, signup, and activation funnels in a developer-facing product.
• Warehouse, modeling, and BI. dbt, a cloud warehouse, reverse-ETL, or a BI layer you built and maintained.
• Attribution modeling. Multi-touch or self-reported attribution that survived real scrutiny.
• Modern GTM tooling. Enrichment and orchestration platforms, sequencing tools, and the judgment to know which of them are worth their cost.
• Fintech or payments. Familiarity with how payments infrastructure is evaluated and who participates in the decision.
• Early-stage build. You have stood up a commercial data stack at a company that had none.

Compensation, location, and benefits

• Base salary: $150,000 - $180,000, depending on experience
• Performance bonus: 10-20%, based on impact to growth
• Equity available
• Benefits: Medical, Dental, Vision, 401K with partial match

Location: Broomfield, Colorado. This role is in-office 3-5 days per week and is available remotely for the right candidate.

Travel: Minimal.

Reports to: Chief Business Officer