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Deepgram is the leading platform for Voice AI, providing real-time APIs for speech-to-text, text-to-speech, and voice agents at scale. The company has processed over 50,000 years of audio and serves 200,000+ developers and 1,300+ organizations including Twilio, Cloudflare, and Vapi.
This governance-focused role owns the integrity and trustworthiness of Deepgram's analytics foundation. As AI agents increasingly drive business decisions, ambiguous or conflicting metric definitions create compounding risk—an agent applies the wrong rule confidently at scale, and nobody catches it. You will prevent that by establishing what the company's numbers mean, making those definitions enforceable in systems, and verifying both people and AI agents use them correctly.
Key responsibilities include: establishing and publishing canonical metric definitions across the business, resolving conflicting definitions by convening stakeholders and driving decisions, implementing agreed definitions in the semantic layer and data catalog so systems enforce them rather than documents describing them, auditing the reporting estate to retire unused assets and establish clear ownership, building data quality checks and agents to monitor freshness, uniqueness, referential integrity, and cross-system reconciliation with failures routed to named owners, maintaining an inventory of AI agents accessing company data and evaluating their output against known-correct answers, and enabling self-serve access to governed, trustworthy data for both direct queries and AI tools.
You will spend most of your time on definition, implementation, and validation work; building pipelines and agentic reporting are secondary. The role demands strong written communication—most of your output is documentation others must trust without re-deriving it. You'll need comfort deprecating and removing work others built, and the ability to navigate organizational complexity to land decisions across functions.
The company operates at the pace of AI with an AI-first mindset. All team members actively use and experiment with advanced AI tools, integrating them into everyday work. Change is rapid, and your day-to-day work will evolve quickly. This role requires comfort with experimentation, adaptation, and continuous learning.
