Marketing & GTM Data Analyst

Deepgram is building the foundational platform for the voice AI economy, powering speech-to-text, text-to-speech, and production voice agents used by over 200,000 developers and 1,300 organizations including Twilio, Cloudflare, and Vapi. The company has processed over 50,000 years of audio and transcribed more than 1 trillion words, establishing unmatched expertise in voice technology. Deepgram operates with an AI-first mindset where every team member actively integrates advanced AI tools into their work, and the pace of change mirrors the speed of AI innovation itself.

Deepgram's go-to-market function is scaling rapidly, and you'll build the analytics and reporting infrastructure from the ground up as the first Marketing and GTM Data Analyst. You'll work closely with the Director of Marketing Operations and cross-functional teams including Decision Intelligence, Data Engineering, Product, Sales Development, Account Executives, and Revenue Operations to create the systems that reveal what's working in the market and where to invest next.

What you'll do

• Design and improve AI agents that automate recurring GTM analytics, including weekly reporting, campaign and sales play readouts, scoring model checks, and anomaly detection, while serving as the GTM expert who writes evaluation cases and quality-assures output.

• Own marketing and GTM KPI definitions and calculation logic, deliver weekly reports that leadership reads, and provide clear narratives explaining what changed and why.

• Measure the full funnel for marketing campaigns and sales plays using pre-post and cohort analysis to determine what to scale, fix, or discontinue.

• Build reusable reporting templates for campaigns and sales plays with consistent definitions, time windows, and attribution rules structured for AI agents to generate first drafts.

• Oversee Dreamdata and evolve multi-touch attribution practices, including sourcing rules, SDR credit, and partner attribution, reconcile them with Salesforce and HubSpot, and translate findings into budget recommendations.

• Evaluate and improve MQL, PQL, and account intent scoring models with automated monitoring of precision, coverage, and conversion lift.

• Define and report partner and business development performance, including partner-sourced pipeline, co-marketing results, and tier performance.

What you'll bring

• Expertise translating complex GTM questions into clear, answerable analytical problems and pushing back constructively when a request needs refinement.

• Deep understanding of the B2B sales funnel and how marketing, product-led, partner, SDR, and AE motions interact.

• Proficiency writing SQL directly in a warehouse and across Salesforce objects including Leads, Contacts, Campaigns, Campaign Members, Opportunities, and custom objects.

• Experience reconciling messy data across multiple systems and fixing data quality problems at the source.

• Hands-on experience building or improving multi-touch attribution setups and reconciling them with CRM data.

• Demonstrated ability to evaluate or tune lead, PQL, or account scoring models and measure their impact on conversion.

• AI-native mindset: you build AI agents or agentic workflows for analytics, automate recurring analysis, and write the evaluation cases and context these systems require to be accurate.

• Track record building dashboards and recurring reports in a BI tool that leadership relies on and acts on, with narratives sized appropriately for executives.

Nice to have

• Three to five years in marketing analytics, GTM analytics, revenue analytics, or a similar analytical role at a B2B SaaS company.

• Hands-on experience with Dreamdata, HockeyStack, Bizible, or comparable attribution platforms, plus HubSpot and Salesforce reporting.

• Python, dbt, semantic layers, or metric store experience.

• Background working with intent data or partner attribution models.

What they offer

• Full-time remote position based in the USA.
• Opportunity to build analytics infrastructure from scratch rather than maintain existing systems.
• Work in an AI-first environment where experimentation and continuous learning are central to the role.