Marketing & GTM Data Analyst

Deepgram•,

1d•$115,000 - $148,000

About The Position

Deepgram is hiring its first Marketing & GTM Data Analyst to build the reporting and analytics function for its go-to-market organization. The analyst will own the answers to what is working and where the company should invest, partnering with the Director of Marketing Operations on the weekly leadership GTM report and collaborating with Decision Intelligence and Data Engineering on AI analytics agents and the GTM data foundation. The work will support Marketing, Product, SDR, AE, Partnerships, and RevOps. The role is AI-first and fast-moving. The analyst is expected to actively use and experiment with advanced AI tools, incorporate them into daily workflows, and adapt as priorities and technology evolve. The position combines GTM measurement, attribution, scoring-model evaluation, data quality, executive reporting, and development of repeatable analytics workflows.

Requirements

Understand the B2B funnel, including MQL, SAL, SQL, and Closed Won, and how marketing, product-led, partner, SDR, and AE motions interact.
Write strong SQL directly in a data warehouse and across Salesforce objects, including Leads, Contacts, Campaigns, Campaign Members, Opportunities, and custom objects.
Reconcile messy data across systems, maintain a high standard for data correctness, and address problems at their source rather than patching them downstream.
Have owned or improved a multi-touch attribution setup and reconciled it with CRM data.
Have evaluated or tuned lead, PQL, or account-scoring models and measured whether they improve conversion.
Operate in an AI-native way by building AI agents or agentic analytics workflows, automating recurring analysis, and writing the evaluation cases and context agents need to be accurate.
Build dashboards and recurring reports in a BI tool, and communicate what the numbers mean.
Own KPI definitions so a metric has a consistent meaning across the organization.
Be comfortable adopting new AI models and modes, experimenting, adapting quickly, and learning continuously.

Nice To Haves

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

Responsibilities

Automate recurring GTM analytics with Decision Intelligence by designing and improving AI agents for weekly reporting, campaign and sales-play readouts, scoring-model checks, and anomaly alerts; write evaluation cases and QA agent output.
Own Marketing and GTM KPI definitions and calculation logic, and prepare the weekly leadership report with a clear explanation of what changed and why.
Measure marketing campaigns and SDR/AE sales plays from first touch through closed-won using pre/post and cohort analyses, and recommend what to scale, fix, or stop.
Create reusable campaign and sales-play reporting templates with consistent definitions, time windows, and attribution rules, structured for AI-generated first drafts.
Own Dreamdata and evolve attribution practices, including multi-touch attribution, sourcing rules, SDR credit, and partner attribution; reconcile attribution with Salesforce and HubSpot and make 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, provide semantic context for AI analytics agents, and drive upstream data-quality fixes.
Turn vague or complex business requests into clear, answerable analytical questions and constructively challenge questions when needed.
Build dashboards and recurring BI reports that stakeholders can rely on and act upon, with reporting narratives appropriate for leadership.