GTM Data Analyst in Bournemouth

Location: Remote (UK time zones preferred) | Type: Full Time | Experience: 3 to 5+ years

About the Role

We're hiring a GTM Data Analyst to build the reporting layer that tells a go-to-market org how it's actually performing. Pipeline, bookings, and revenue data currently live across a CRM, a forecasting tool, and an ERP. Your job is to bring them together in Snowflake into one dimensional model that sales, marketing, and finance all trust.

The client is an enterprise analytics software company whose database sits underneath real-time decision making at global investment banks, aerospace and defense programs, high-tech manufacturers, and life sciences organizations. They're growing both organically and through acquisition, which is exactly why a reliable GTM data layer matters right now.

This is a green-field build. The reporting layer and the metric definitions behind it don't exist yet, so you'll define them from first principles and then get the business to agree on them. If you've ever sat in a forecast call where three teams quoted three different pipeline numbers off the same data, you already understand the problem you'd be solving.

What You'll Do

• Shape the GTM dimensional models in Snowflake, translating reporting needs into facts and dimensions built on CRM, forecasting, and ERP sources.
• Work inside an existing medallion architecture alongside the team that owns the broader platform.
• Define the canonical set of GTM metrics, so pipeline coverage, SQO, and net revenue retention mean the same thing to a rep and to the CFO.
• Use AI tooling such as Claude Code as your default way of querying, modeling, and documenting, not as an occasional experiment.
• Build the data quality checks and reconciliation processes that catch anomalies before anyone sees them on a dashboard.
• Chase root causes upstream with the RevOps and finance owners of the source systems rather than patching the symptom downstream.
• Turn shifting questions from Sales, Marketing, and FP&A into clear narratives, delivered through dashboards that get used in business reviews and forecasting.
• Partner with RevOps and Marketing Ops on definitions, ownership, and the buy-in that makes them stick.

Qualifications

• Data Modeling and Platform
  • 3 to 5+ years building analytical data models in Snowflake or a comparable cloud data warehouse.
  • Comfortable working within an established platform and medallion layers rather than standing up your own stack.
  • You can build a star or snowflake schema and explain the design choices to someone who doesn't work in data.
  • A habit of interrogating source data instead of assuming it's clean.
• GTM and Commercial Fluency
  • Working knowledge of the CRM object model (leads, contacts, accounts, opportunities, campaigns) and how funnel metrics map onto it.
  • Familiar with MQL-to-SQO conversion, pipeline velocity, and what separates pipeline volume from pipeline quality.
  • Understand ARR, ACV, and quota structures well enough to know why a metric matters, not just how to calculate it.
  • Track record translating a question from a CRO or VP of Marketing into an answer they can act on.
• Nice to Have
  • Python for automation and scripting data quality checks.
  • Familiarity with marketing automation platforms and the ways data gets distorted at the point of capture.
  • Attribution and funnel modeling, including first-touch versus last-touch trade-offs.
  • Forecasting fundamentals such as pipeline coverage ratios and cohort-based forecasting.
  • Power BI for dashboard builds and maintenance.

Why Join Us

You'd report into the Head of Strategic Data and GTM, with real ownership over the ambiguity: metric definitions, dimensional design, and the stakeholder alignment that makes any of it usable. Modern tooling, no legacy debt to unwind, and an expectation that you build with AI from day one.

What you create here becomes the shared source of truth for how the business measures itself. That's high visibility, and it only works if you care as much about earning trust across teams as you do about the model underneath.