[All remote jobs](https://jobicy.com/jobs.md)[![Deepgram logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2020/12/WRILS-201212160008-406229.jpg)](https://jobicy.com/company/deepgram.md)[Deepgram](https://jobicy.com/company/deepgram.md) # Senior Data Intelligence Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/deepgram.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 165k–205k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished31 Oct 2026Apply before30 Nov 2026Listing views40Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Deepgram is seeking a senior founding engineer for its Data Intelligence team to build the data and agent tooling that powers autonomous business intelligence. The role centers on production-grade SQL, Python, dbt models, semantic layers, and permissions-aware tools for querying systems such as Athena and Salesforce. This engineer will connect data, engineering, and infrastructure teams while developing AI-enabled workflows for usage-based SaaS revenue, churn, reliability, and audio intelligence. The position requires at least five years of data or AI engineering experience, proven RAG or agentic-system delivery, and comfort operating in fast-changing, incomplete infrastructure. It is a high-impact individual-contributor role with substantial ownership of foundational systems. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a founding, senior-level data engineering role involving agentic AI systems, complex usage-based revenue data, and production reliability requirements. The engineer must independently make architectural decisions amid rapidly evolving priorities and incomplete infrastructure. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$185,000US market range (per year)$160k–$220k0$242.00k AI insightThe employer discloses a yearly base compensation range of $165,000–$205,000 USD, with a midpoint of $185,000. An estimated US market range for a senior data intelligence or AI data engineering role is $160,000–$220,000 per year; actual pay may vary by location, experience, and equity or bonus structure. ## Core skills Skills and capabilities most closely associated with this opportunity. [SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Data modeling](https://jobicy.com/jobs?search_keywords=Data%20modeling.md)[Semantic layer design](https://jobicy.com/jobs?search_keywords=Semantic%20layer%20design.md)[AI agents](https://jobicy.com/jobs?search_keywords=AI%20agents.md)[RAG systems](https://jobicy.com/jobs?search_keywords=RAG%20systems.md)[Amazon Athena](https://jobicy.com/jobs?search_keywords=Amazon%20Athena.md)[Salesforce integration](https://jobicy.com/jobs?search_keywords=Salesforce%20integration.md)[Data contracts](https://jobicy.com/jobs?search_keywords=Data%20contracts.md)[Usage-based SaaS analytics](https://jobicy.com/jobs?search_keywords=Usage-based%20SaaS%20analytics.md)[Revenue analytics](https://jobicy.com/jobs?search_keywords=Revenue%20analytics.md)[Data infrastructure](https://jobicy.com/jobs?search_keywords=Data%20infrastructure.md)[Automation](https://jobicy.com/jobs?search_keywords=Automation.md)[Speech-to-text data processing](https://jobicy.com/jobs?search_keywords=Speech-to-text%20data%20processing.md) Sample interview questionsHow would you design a semantic layer for a usage-based SaaS business that AI agents can query safely?I would define version-controlled dbt models around agreed business entities and metrics, such as accounts, usage events, subscriptions, revenue, gross margin, and NRR. I would document metric definitions, lineage, freshness, ownership, and access constraints, then expose a governed query interface with row- and column-level permissions. For agents, I would provide constrained tools, validated query templates, metadata retrieval, auditing, and evaluation tests to prevent unsafe or inaccurate interpretations. Describe an agentic or RAG system you built on top of structured data. I would explain the problem, source systems, and why an agentic or retrieval-based approach was appropriate. A strong implementation would combine trusted metadata and curated data models with tool-based SQL access, permission enforcement, observability, and answer-quality evaluation. I would also quantify impact, such as reduced analyst turnaround time, improved self-service adoption, or higher accuracy for recurring business questions. How would you investigate a potential churn signal by combining Salesforce information, product consumption, and API latency data? I would first establish a shared account and time-grain model across the three sources. I would compare changes in usage, support or opportunity signals, contract context, and latency or error trends against an account baseline and relevant peer cohort. I would surface evidence with confidence levels, preserve lineage to each source, and validate findings with customer-facing and engineering stakeholders before operationalizing alerts. What practices would you use to make data contracts reliable for downstream AI tools? I would establish explicit schemas, ownership, service-level expectations, freshness thresholds, and compatibility rules for each important dataset. Automated checks would validate schema changes, completeness, quality, and metric reconciliation before release. I would also publish machine-readable metadata and deprecation processes so both humans and agents can use stable interfaces. How do you prioritize automation opportunities in a fast-moving data organization? I prioritize work by business impact, frequency of the manual task, error or compliance risk, and feasibility of a durable solution. I start with repetitive workflows that block decisions or create inconsistent metrics, instrument the current process, and deliver a narrowly scoped automation with clear success measures. After validating adoption and reliability, I expand it into a reusable platform capability rather than creating one-off scripts. Opportunity details ## About this role. ### 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 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. 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. 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