Senior Data Intelligence Engineer

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:

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

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

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

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:

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.

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.

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

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.

About Deepgram

Deepgram is the foundational AI company for voice. We build the models that allow machines to hear, understand, and speak to humans with zero latency. As we scale our usage-based economy, we are building the "Intelligence Layer"—a system of autonomous agents that monitor, reason, and act on our data to drive NRR and operational excellence.

The Role

Reporting to the Head of Decision Intelligence , you are the founding engineer of the Data Intelligence team. While the Head of Data sets the strategic "Brain" of the company, you build the nervous system. You are not building legacy dashboards; instead you are building the API-based tools, dbt models, and semantic layers that allow AI agents to navigate our data environment autonomously.

Key Responsibilities

Semantic Layer Architecture: Build and maintain high-fidelity dbt and SQL models that serve as the "ground truth" for our complex, usage-based revenue models.

Agent Tooling & Integration: Develop the tools and permissions frameworks that allow "Analyst Agents" to query Athena, correlate Salesforce churn signals, and identify API latency issues.

Infrastructure Evolution: Serve as the technical bridge to the Engineering/Infra team to ensure our data contracts are "agent-ready" and highly reliable.

The "Unlock Audio Intelligence" Project: Partner with the Head of Data to ingest thousands of hours of internal calls using Deepgram’s own models, turning unstructured audio into queryable insights for GTM teams.

Automation Obsession: Maintain a culture where manual, repetitive SQL tasks are viewed as automation bugs to be solved with code and agents.

Requirements

Experience: 5+ years in high-growth Data or AI Engineering roles.

AI-Native Technical Skills: Mastery of SQL and Python (production-grade) with a proven track record of building RAG or Agentic systems on top of structured data.

Usage-Based SaaS Fluency: Familiarity with the metrics of an API-first business, such as consumption patterns, gross margins, and NRR.

Builder Mindset: Comfort with "work-in-progress" infrastructure and the ability to turn messy, siloed data into high-impact business logic.

Bonus Points

Prior experience as a Software Engineer or Data Engineer.

Experience with real-time billing and consumption systems.

Contributions to open-source data or AI projects.

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