AI数据准备负责人
AI Data Readiness Lead
## **公司简介**
Deepgram 是支撑新兴万亿美元语音 AI 经济的领先平台，提供实时 API 用于语音转文本（STT）、文本转语音（TTS），并可大规模构建生产级语音代理。超过 20 万名开发者和 1300 多家组织正在构建“由 Deepgram 提供支持”的语音产品，包括 Twilio、Cloudflare、Sierra、Decagon、Vapi、Daily、Cresta、Granola 和 Jack in the Box。Deepgram 的语音原生基础模型通过云 API 或自托管及本地软件进行访问，具有无与伦比的准确性、低延迟和成本效率。由领先的全球投资者和战略合作伙伴主导的最新 C 轮融资支持，Deepgram 已处理超过 5 万年的音频，并转录了超过 1 万亿个单词。世界上没有哪家公司比 Deepgram 更了解语音。
## **公司运营节奏**
在 Deepgram，我们期望以 AI 为先的思维方式——AI 的使用和熟悉度不是可选的，而是我们运作、创新和衡量绩效的核心。
在 Deepgram 工作的每位团队成员都应积极使用和试验先进的 AI 工具，甚至将它们构建到日常工作中。我们衡量 AI 在交付结果中的应用效果，持续且富有创意地使用最新 AI 能力是这里成功的关键。候选人应能够快速采用新模型和新模式，将 AI 整合到工作流程中，并不断推动这些技术的边界。
此外，我们以 AI 的速度前进。变化迅速，你每天的工作内容也会随之快速变化。如果你不热衷于实验、适应、灵活思考和不断学习，或者你寻求的是高度指导性的传统朝九晚五工作，那么这可能不是适合你的职位。
### 关于该职位
分析只有在底层定义可信的情况下才可靠。随着越来越多的报告和决策转向 AI 代理，模糊或冲突的指标定义成本会成倍增加，一个代理可能会自信地大规模应用错误规则，而无人察觉。
该职位的存在就是为了防止这种情况。你将负责我们的数据含义，使这些定义在服务它们的系统中可执行，并验证人员和代理是否正确使用它们。
这是一个以治理为核心的职位，具有真正的技术深度。你将花时间定义、实施和验证，构建管道
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## **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
Analytics is only as trustworthy as the definitions underneath it. As more reporting and decision-making moves to AI agents, the cost of ambiguous or conflicting metric definitions compounds, an agent applies the wrong rule confidently, at scale, and nobody catches it.
This role exists to prevent that. You will own what our numbers mean, make those definitions enforceable in the systems that serve them, and verify that both people and agents are using them.
This is a governance-first role with real technical depth. You will spend your time defining, implementing, and validating, building pipelines and developing agentic reporting are secondary.
### What you'll work on
**Own the metric registry.** Establish canonical definitions for the metrics the business runs on. Where competing versions exist, convene the owners, document the disagreement, and drive to a decision. Publish changes with a clear statement of what moves and why.
**Make definitions enforceable.** Implement agreed definitions in the semantic layer and data catalog so they are applied by the system rather than described in a document. Retire superseded versions.
**Reduce the surface area.** Audit the reporting estate, retire assets with no audience, and establish ownership for what remains.
**Build data quality checks/agents.** Freshness, uniqueness, referential integrity, and cross-system reconciliation — with failures routed to named owners/agents who act on them.
**Verify AI agents.** Maintain an inventory of agents accessing company data and the definitions each relies on. Evaluate agent output against known-correct answers and track accuracy, refusal, and error rates.
**Enable self-serve.** Make governed data accessible and trustworthy for people querying it directly or through AI tools.
### Qualifications
- 5+ years in analytics, analytics engineering, or a closely related field
- Strong SQL, including comfort reverse-engineering undocumented transformation logic written by others
- Direct ownership of a semantic or metrics layer in production — dbt, Cube, LookML, or equivalent. Not just usage: responsibility for what went into it and why
- Demonstrated ability to resolve conflicting metric definitions across functions and land a decision
- Clear written communication. Most of your output is documentation others must trust without re-deriving it
- Comfort deprecating and removing work that others built
- Experience evaluating LLM or AI agent output against ground truth
- Experience developing or contributing to a data catalog and/or lineage tooling
### Nice to have
- Experience with lakehouse architectures, Iceberg, Athena, Trino, or similar
- Exposure to audit readiness, SOX, or financial controls environments
- Consumption or usage-based business models, where committed, consumed, invoiced, and recognised revenue are genuinely different numbers
- Having joined a function early, before process existed
_**Notice**: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @_ [_deepgram.com_](http://deepgram.com) _email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com._