Senior Analytics Engineer at Deepgram – AI Data Layer

Deepgram; United States; Remote; USD 160,000 - 220,000; Full time

Job summary

Deepgram is building its decision layer to provide a conformed, reliable data surface for AI-driven analytics. You will own the semantic and metrics layer and craft agent-facing tooling to enable analysts and agents to get accurate answers from a unified model.

This senior IC role emphasizes autonomy and ownership, with cross-functional collaboration across sales, product, and finance. Remote in the US, competitive compensation is typical for senior AI analytics roles.

Qualifications

• Design semantic/metrics layer across dbt, Cube and LookML.
• Develop agentic data tooling and MCP surface design.
• Resolve cross-system identities across Salesforce, Stripe and billing systems.
• Create evaluation frameworks for LLM/agent output accuracy.

Responsibilities

• Model the gold layer: conformed entities across customer, account, project and contract.
• Own the semantic layer: define and version metrics used by dashboards and notebooks.
• Build the agent-facing surface: tooling, permissions and MCP surfaces.
• Make correctness checkable: assertions on grain, uniqueness and reconciliation.
• Absorb the questions: answer sales, product and finance questions with a reusable model.

Skills

Semantic layer design

Agentic tooling

Identity resolution

LLM evaluation

Tools

dbt

Cube

LookML

Job description

Senior Analytics Engineer at Deepgram – AI Data Layer — Remote

Career Growth Signal: This is a rare "first or second in" senior IC role at a well-funded AI infrastructure company (5,000+ enterprise customers, 650,000+ developers). Being foundational on a new team means high ownership, high visibility, and a direct line to shaping the data decision layer for the entire org — a career-defining opportunity for the right engineer.

Skills You'll Build:

• Semantic/metrics layer design (dbt, Cube, LookML) — extremely in-demand as the "headless BI" and AI analytics markets explode
• Agentic data tooling and MCP surface design — a genuinely emerging and scarce skill set
• Cross-system identity resolution across Salesforce, Stripe, and billing systems — critical for any SaaS analytics role
• Evaluation frameworks for LLM/agent output accuracy — a hot, transferable skill in 2024–2025

Compensation Context: No salary is listed, but senior data engineers at AI-native startups of this profile typically command $160,000–$220,000 USD annually in the US market. Deepgram's customer list (Twilio, Cloudflare) suggests strong revenue traction and Series B+ funding runway.

Green Flags: Clear operating principles, explicit AI-first culture, and high autonomy for ICs signal a mature-but-fast startup culture. The interview process (walking through your own AI workflow) is refreshingly meritocratic.

Red Flags to Watch: "Nothing here is finished" and "no ticket queue" can mean exciting greenfield work — or chronic under-resourcing. Clarify team headcount plans and data platform maturity before accepting.

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.

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 the Role

Deepgram is building its decision layer: one governed set of numbers that people and AI agents can act on without re-deriving them. This role builds it.

You are not building pipelines. Ingestion, orchestration and latency belong to the data platform team, and they're good at it. You take what lands in the warehouse and turn it into the layer above the conformed gold tables — the semantic models and the agent-facing tools that let someone (or something) ask a question and get an answer that holds up.

This is a senior technical IC role on a new team. You'll be the first or second engineer in.

What You'll Work On

Model the gold layer. Conformed entities for customer, account, project, product and contract. Our identity model spans Salesforce, the console, Stripe and the billing system, with no authoritative key between them — resolving that is week one.

Own the semantic layer. Author the models that define our metrics once, so a dashboard, a notebook and an agent all return the same number. Version them. Retire the superseded ones.

Build the agent-facing surface. The tools, permissions and MCP surfaces that let analyst agents navigate our data without inventing a join that looks plausible and is wrong.

Make correctness checkable. Assertions on the things that break silently — grain, uniqueness, cross-system reconciliation — routed to a named owner. Evaluation of agent output against known-correct answers, tracking accuracy, refusal, and confidently-wrong rates separately.

Absorb the questions. Sales, product and finance bring real questions. You answer them, and you leave behind a model that makes the next version of that question cheaper.

What We're Looking For

AI-native practice, demonstrated. You use AI daily and intricately — not to autocomplete, but as a way of working that has changed what you attempt. We will ask what the most interesting thing you've built with AI in the last month is, and we'll go three questions deep on the answer.

Production-grade SQL and Python. Code that ships on a schedule, has broken, and was found by your own instrumentation rather than by a stakeholder.

Semantic or metrics layer ownership. dbt, Cube, LookML or equivalent — owned, not just used. You've changed a definition people already depended on, and can say who you told and what the number moved by.

Modelling judgement over tool knowledge. You reason about grain and keys before you write the query. You can tell a data defect from an undecided business definition and treat them differently.

Comfort in work-in-progress infrastructure. Nothing here is finished. There is no ticket queue to work from and no process to inherit — you'll be building both as you go.

Clear writing. Much of the output is a definition or a contract someone else has to trust without re-deriving it.

Bonus Points

Usage-based or consumption business models, where committed, consumed, invoiced and recognised revenue are genuinely different numbers.

Real-time billing and consumption systems.

Lakehouse architectures — Iceberg, Athena, Trino or similar.

Contributions to open-source data or AI projects.

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