Marketing & GTM Data Analyst

Company Operating Rhythm

At Deepgram, we expect an AI-first mindset-AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance.

Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do.

Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5.

Team Overview

Marketing Operations keeps Deepgram's go-to-market engine measurable. As our first Marketing & GTM Data Analyst, you'll build the reporting and analytics function rather than inherit a finished one, and own the answer to what's working and where to invest next. You'll partner with the Director of Marketing Operations on the weekly GTM report leadership reads, and with our Decision Intelligence and Data Engineering teams on AI analytics agents and the GTM data foundation. Your work will power Marketing, Product, SDR, AE, Partnerships, and RevOps.

Key Goals

• Automate recurring GTM analytics with Decision Intelligence, designing and improving AI agents for weekly reporting, campaign and sales play readouts, scoring-model checks, and anomaly alerts, as the GTM expert who writes evaluation cases and QAs their output.
• Own Marketing and GTM KPI definitions, calculation logic, and the weekly report leadership reads, with a clear narrative on what changed and why.
• Measure marketing campaigns and SDR/AE sales plays from first touch through closed-won, using pre/post and cohort analysis to say what to scale, fix, or stop.
• Create reusable campaign and sales play reporting templates with consistent definitions, time windows, and attribution rules, structured so AI agents can generate first drafts.
• Own Dreamdata and evolve our attribution practices (multi-touch, sourcing rules, SDR credit, partner attribution), reconcile them with Salesforce and HubSpot, and turn attribution into budget recommendations.
• Evaluate and improve MQL, PQL, and account intent scoring models, with automated monitoring of precision, coverage, and conversion lift by score.
• Define and report partner and business development performance, including partner-sourced and influenced pipeline, co-marketing results, and performance by tier.
• Define GTM source-of-truth datasets with Data Engineering and Decision Intelligence, write the semantic context AI analytics agents rely on, and drive data quality fixes upstream.

Minimum Skills, Knowledge & Capabilities

• Turns vague or complex requests ("did re:Invent work?", "why is pipeline soft?") into clear, answerable questions before touching data, and pushes back constructively when the question is wrong.
• Understands the B2B funnel (MQL, SAL, SQL, Closed Won) and how marketing, product-led, partner, SDR, and AE motions interact, and explains why the numbers look the way they do.
• Writes strong SQL directly in a warehouse and across Salesforce objects (Leads, Contacts, Campaigns, Campaign Members, Opportunities, custom objects).
• Reconciles messy data across systems, holds a high bar for what counts as right, and fixes problems at the source rather than patching downstream.
• Has owned or improved a multi-touch attribution setup and reconciled it with CRM data.
• Evaluates or tunes lead, PQL, or account scoring models and measures whether they improve conversion.
• Operates AI-native: builds AI agents or agentic workflows for analytics (e.g., Claude Code, MCP servers, n8n), automates recurring analysis, and writes the evaluation cases and context agents need to be accurate.
• Builds dashboards and recurring reports in a BI tool that people rely on and act on, with a narrative sized for leadership.
• Owns KPI definitions so the same number means the same thing everywhere.

Preferred Qualifications (Nice-to-have, Not Required)

• Typically ~3-5 years in marketing analytics, GTM or revenue analytics, or a similar analytical role at a B2B SaaS company, or equivalent demonstrated experience.
• Hands-on with Dreamdata, HockeyStack, Bizible, or a similar attribution tool, plus HubSpot and Salesforce reporting.
• Python, and experience with dbt, semantic layers, or metric stores.
• Worked with intent data or partner attribution.
• Experience at a usage-based, developer-focused, or product-led business.