Marketing & GTM Data Analyst

Company Overview

Deepgram is bringing the world’s most natural interface, voice, to the technology we use every day. Our speech models and real-time infrastructure power AI systems that keep up with the way people actually speak. More than 5,000 organizations run conversational voice AI on Deepgram in production, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Cresta, Granola, and Jack in the Box.

The Deepgram Voice Agent Platform brings together speech-to-text, text-to-speech and speech-to-speech, with voice-native foundation models available through cloud APIs or self-hosted and on-premises deployments. Built for accuracy, low latency and cost efficiency, Deepgram makes high-quality voice experiences practical to build and run at scale. More than 650,000 developers have used Deepgram to process more than 200,000 years of audio. Every day, more of the world’s conversations with AI run on our models.

Operating Principles:

Deepgram is a research company that productizes as fast as possible. Four Operating Principles shape how we think, decide, and work together:

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Compress before you communicate. You think it through first, then lead with the bottom line.

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Resist entropy. You question what already exists before adding more process or tools.

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Solve the category. You fix recurring problems at the source, not one case at a time.

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Work at the frontier. You go after problems no one has solved yet.

We look for people who already work this way, and these principles shape how we grow and evaluate our team.

AI-First by Default

We build AI that millions of people talk to, and we run the company the same way. AI is how we centralize work here, and is core to how we operate.

That means more than using the tools. We expect you to:

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Build, not just use. Design your own workflows and agents, and use AI to remove whole categories of work instead of doing the same work faster.

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Own the output. AI can speed up the work. It doesn’t make anyone less accountable for it. You understand, verify, and stand behind everything you share, including what an agent produces for you.

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Spend the time you get back at the frontier. The capacity AI creates goes toward problems no one has solved yet.

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Keep pace. Models and tools change every month, and so will your work. You adopt what’s new quickly and drop what no longer earns its place.

We judge AI by what it helps you deliver and whether you stand behind the result, not by how often you use it. In interviews, expect to walk us through a workflow you built and where you drew the line between AI and your own judgment.

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

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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.

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Own Marketing and GTM KPI definitions, calculation logic, and the weekly report leadership reads, with a clear narrative on what changed and why.

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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.

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Create reusable campaign and sales play reporting templates with consistent definitions, time windows, and attribution rules, structured so AI agents can generate first drafts.

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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.

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

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

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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

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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.

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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.

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Writes strong SQL directly in a warehouse and across Salesforce objects (Leads, Contacts, Campaigns, Campaign Members, Opportunities, custom objects).

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Reconciles messy data across systems, holds a high bar for what counts as right, and fixes problems at the source rather than patching downstream.

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Has owned or improved a multi-touch attribution setup and reconciled it with CRM data.

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Evaluates or tunes lead, PQL, or account scoring models and measures whether they improve conversion.

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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.

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Builds dashboards and recurring reports in a BI tool that people rely on and act on, with a narrative sized for leadership.

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Owns KPI definitions so the same number means the same thing everywhere.

Preferred Qualifications

(nice-to-have, not requi