Marketing Operations Lead

Rho is the modern banking platform built for the AI era. Startups and growth-stage companies can open accounts in minutes, issue cards, manage expenses, pay bills, and close the books – all in one connected platform backed by real human support.

We're hiring a Marketing Operations Lead to own the data and systems layer underneath Rho's marketing team. Today that layer is split across a dozen platforms, most of it unmodeled, and nobody owns it full time. The result is that we can tell you what we spent and roughly what came in, but not reliably which channels produce funded customers at what cost. Your job is to close that gap and keep it closed. You'll define what marketing measures, get every source into Snowflake as modeled data, and build the reporting that the marketing team, the exec team, and the board actually run on. This is a hands-on, single-threaded role. You work in the same data infrastructure as the rest of the company: Snowflake and dbt, alongside our data engineering team. You are explicitly not here to build a parallel marketing data stack inside Salesforce, HubSpot, or a spreadsheet. If it matters, it lives in the warehouse. You partner with data engineering rather than reporting into it. They own the platform and the pipelines. You own the definitions, the marketing models, the activation layer, and whether the numbers are right. You're accountable for whether marketing can answer its own questions without waiting on anyone, and for whether spend decisions get made on data that holds up. Not for closed tickets or built dashboards.

Own the marketing metrics layer: define the grain, the metric definitions, and the join path to funnel and funded outcomes for every marketing source. This is the current bottleneck, and it becomes yours on day one. Land and model the full source list in Snowflake: paid social, web analytics, search console, organic social across four platforms, affiliate and referral, call platform data, CRM lead and opportunity data, and AI search visibility. Work with data engineering on ingestion; own the dbt models on top. Build channel-level attribution that survives scrutiny, including reconciling the known gaps between ad platform reporting, the CRM, and our call platform, where the same funnel currently returns three different numbers. Ship and maintain the recurring reporting the business runs on: weekly channel performance and cost per funded customer, CAC and payback by channel and cohort against target, and the marketing health view. Own the activation layer: reverse ETL out of the warehouse into lifecycle, ads, and sales tooling, so audiences and triggers are built from modeled data rather than hand-pulled lists. Own marketing systems hygiene end to end: form capture, lead routing and CRM sync, UTM and campaign taxonomy, and the data contracts between marketing tools and the warehouse. Make PII handling correct by design in the marketing stack, including where lead capture data is allowed to land and what gets passed to third-party platforms. Instrument new programs as they launch rather than after, so a channel is measurable the week it goes live. Leave every model and pipeline with documentation and monitoring, so a wrong number surfaces on a dashboard before it surfaces in a board meeting.

You write SQL fluently and have built and owned dbt models in a real warehouse. This is not a role you can do from platform UIs. You've owned marketing or GTM data at a company where the answer mattered financially, and you've been the person who had to defend a CAC or attribution number to a skeptical executive. You've reconciled conflicting numbers across ad platforms, a CRM, and a warehouse, and can explain why they differ rather than picking the flattering one. Experience with the systems side, not just analysis: forms, lead routing, CRM sync, reverse ETL, campaign taxonomy. You're comfortable owning the plumbing. You define metrics