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Vapi is a Series A voice AI platform powering 1 billion calls for companies like Amazon Ring, Intuit, and ServiceTitan, trusted by 1 million developers. You will be Vapi's first Head of Data, building the data function from scratch and reporting to the CFO.
You will own the single source of truth across the company: define and maintain Vapi's core metric set (ARR, NRR, churn, call volume, latency, reliability) and build the canonical data model that every team pulls from. You'll make the GTM funnel measurable end-to-end, from signup through activation, PQL, MQL, SQL, PoC, and closed-won to time-to-live, adoption, expansion, and retention. You'll run the operating cadence, owning data, dashboards, and narrative for weekly and monthly business reviews, and instrument OKRs with reliable, current data.
You will build and scale the modern data stack end-to-end: warehouse, ingestion, transformation, orchestration, and BI. You'll ship well-documented, tested dbt models and enforce data contracts between producers and consumers. You'll design pipelines for call telemetry, transcript events, usage metering, billing signals, and model performance traces at voice-API scale, setting freshness SLAs and alerting so data issues surface before they become decision errors.
You'll build AI/LLM observability and monitoring pipelines, unlock self-serve analytics by creating canonical and semantic layers for Sales, Finance, CS, Product, and Engineering, and own governance, quality, and access controls including data dictionary, lineage, and compliance with HIPAA, GDPR, CCPA, and customer DPAs. You'll be a strategic partner on pricing, market expansion, product bets, and customer health, and you'll build the team by hiring the first data engineers and analysts.
Success milestones: 30 days—audit current sources of truth and publish metric definitions v1; 90 days—canonical ARR, NRR, and billable-call models in production; 6 months—end-to-end GTM funnel and self-serve dashboards live; 1 year—leaders trust the numbers without question.
REQUIREMENTS:
- 10+ years in data, analytics, or data engineering, including building or leading a data function at a high-growth technology company
- Shipped and sustained a company-wide single source of truth: aligned conflicting definitions, won over resistant stakeholders, kept trust in numbers over time
- Deep fluency in modern data stack: dbt, Databricks, Fivetran or PostHog, BI layer like Hex; strong SQL baseline; Python comfort a plus
- Bias to action: ship working dashboards before perfect ones, then iterate
- Built pipelines handling billions of event-driven rows; know failure modes
- Partner equally well with Finance, Product, Engineering, GTM; earn trust through listening, precise scoping, on-time delivery
- Sharp judgment on build-versus-buy, technical debt, and right-sizing solutions
- Clear, direct communicator: explain metric discrepancies to CFOs and pipeline architecture to data engineers
- Experience with AI/ML data infrastructure: feature stores, model evaluation pipelines, or LLM observability
BONUS:
- First or founding data leader at Series A–C company
- Background at developer-facing platform, API business, or usage-based SaaS
- Familiarity with usage-based billing data models and revenue metering
- Hands-on HIPAA or GDPR compliance in analytics context
- Built data products for external customers
