Senior Analytics Engineer - Decision Intelligence

Company Overview Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. 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. 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 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