Vapi (/ˈVɑːpi/):
Voice AI that resolves, not transfers
Powering 1 billion calls for companies like Amazon Ring, Intuit, ServiceTitan, and New York Life
Trusted by 1 million developers building the future of voice agents
Backed by Peak XV, Bessemer, Kleiner Perkins, M12, Y Combinator, and more with $72M raised
Try talking to Vapi now!
Vapi has more data than clarity. One billion calls, millions of developer events, and an enterprise book growing 10x, but no single source of truth.
Finance, GTM, Product, and Engineering each have their own numbers. Alignment breaks down exactly when it matters most.
You will be our first Head of Data. You will build the function from scratch: the canonical metrics, the infrastructure behind them, and the culture that trusts them.
This is a leadership role reporting to the CFO. Every function's ability to move fast and move together depends on the work you do.
Own the single source of truth. Define and own Vapi's core metric set (ARR, NRR, churn, call volume, latency, reliability) and build the canonical data model every team pulls from. One definition of a billable call. One definition of an active customer. One number in the board deck.
Make the GTM funnel measurable end to end. From signup and first call, through activation, PQL, MQL, SQL, SQO, PoC, and closed won, to time-to-live, adoption health, expansion, and retention. Arbitrate conflicting definitions and drive adoption of shared logic.
Run the operating cadence. Own the data, dashboards, and narrative behind weekly and monthly business reviews, and instrument OKRs with reliable, current data.
Build and scale the data platform. Own the modern data stack end to end (warehouse, ingestion, transformation, orchestration, BI). Ship well-documented, tested dbt models and enforce data contracts between producers and consumers.
Handle voice-API scale. Design pipelines for call telemetry, transcript events, usage metering, billing signals, and model performance traces. Set freshness SLAs, alerting, and on-call coverage so data issues surface before they become decision errors.
AI/LLM observability and monitoring. Build pipelines for AI/LLM data pipeline monitoring, model telemetry, and prompt-to-performance observability.
Unlock self-serve analytics. Build canonical and semantic layers where Sales, Finance, CS, Product, and Engineering answer their own questions. Ship function-specific dashboards: GTM funnel and PoC win rate, revenue and cohorts, reliability and latency, activation and PQL rate.
Own governance, quality, and access. Build the definitions library, data dictionary, lineage, and access controls. Partner with Security and Legal on retention, residency, and PII and voice-data handling under HIPAA, GDPR, CCPA, and customer DPAs.
Be a strategic partner. Bring data into pricing, market expansion, product bets, and customer health. Partner with Finance on revenue recognition, usage-based billing, and investor reporting. Partner with GTM on pipeline health, territory design, and retention modeling.
Build the team. Own headcount planning, hire the first data engineers and analysts, and set the bar for high-craft, high-trust data work. Make build-versus-buy calls and own data vendor relationships.
What Success Looks Like:
First 30 days: Audit every source of truth today. Publish a metric definition v1 and a prioritized roadmap agreed with the CFO and leadership team.
First 90 days: One canonical model for ARR, NRR, and billable calls in production. Weekly business review runs on your data.
First 6 months: GTM funnel measurable end to end. Self-serve dashboards live for each function. First hires on the team.
First year: Leaders trust the numbers without asking where they came from. Data is a competitive advantage, not a shared frustration.
10+ years in data, analytics, or data engineering, including building or leading a data function at a high-growth technology company.
You have shipped and sustained a company-wide single source of truth: aligned conflicting definitions, won over resistant stakeholders, and kept trust in the numbers over time.
Deep fluency in the modern data stack: dbt, Databricks, Fivetran or PostHog, and a BI layer like Hex. Strong SQL is a baseline; Python comfort is a plus.
Bias to action. You ship a working dashboard before you build the perfect one, then iterate.
You have built pipelines that handle billions of event-driven rows and know where the failure modes hide.
You partner equally well with Finance, Product, Engineering, and GTM. You earn trust by listening, scoping precisely, and delivering on time.
Sharp judgment on build-versus-buy, technical debt, and when good enough is right. You do not over-engineer for a company this size.
Clear, direct communicator. You can explain a metric discrepancy to a CFO and a pipeline architecture to a data engineer in the same day.
Experience with AI or ML data infrastructure: feature stores, model evaluation pipelines, or LLM observability.
Bonus Points:
First or founding data leader at a Series A through C company.
Background at a developer-facing platform, API business, or usage-based SaaS company.
Familiarity with usage-based billing data models and revenue metering systems.
Hands-on HIPAA or GDPR compliance in an analytics context.
Built data products for external customers, not just internal stakeholders.
Build something worthy of love
Craft matters. We aim to build products and experiences customers genuinely love, not just tolerate.
Commit and follow through
We finish what we start and build trust by being people others can count on.
Why not today?
We value urgency and momentum. The fastest path to customer value usually wins.
Seek raw input
We go directly to customers, data, and teammates instead of relying on summaries or assumptions.
It’s our problem
We operate as one team. We share credit, own mistakes together, and support each other when things get hard.
Be direct and kind
We give feedback clearly, respectfully, and without delay.
Generational impact: Build the human interface for every business
Ownership culture: Many of us are previous founders
Kind team: The founders, Jordan and Nikhil, are Canadians
Tier-1 Investors: YC, KP seed, Bessemer Series A
Real stake: We offer a competitive salary and excellent equity ownership
Comprehensive health coverage: medical, dental, and vision plans
Team love: We love hanging out, and we do quarterly off-sites
Flexible time off: take what you need
More: catered meals, transportation, gym, and a $10k annual L&D budget
Skills Required
- 10+ years of experience in data, analytics, or data engineering
- Experience building or leading a data function at a high-growth technology company
- Experience establishing a company-wide single source of truth and aligning conflicting metric definitions
- Deep fluency with modern data stack technologies including dbt, Databricks, Fivetran or PostHog, and a BI layer such as Hex
- Strong SQL skills
- Experience building pipelines handling billions of event-driven rows
- Ability to partner effectively with Finance, Product, Engineering, and GTM
- Experience with AI or ML data infrastructure, such as feature stores, model evaluation pipelines, or LLM observability
- Python familiarity
- Experience as a first or founding data leader at a Series A through C company
- Background at a developer-facing platform, API business, or usage-based SaaS company
- Familiarity with usage-based billing data models and revenue metering systems
- Hands-on HIPAA or GDPR compliance experience in analytics
- Experience building data products for external customers
Vapi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Vapi and has not been reviewed or approved by Vapi.
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Equity Value & Accessibility — Equity is presented as meaningful ownership in a fast‑growing startup, with substantial grants emphasized and an explicit “ownership culture.” This positioning gives employees a real financial stake in the company’s growth.
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Healthcare Strength — Health coverage is described as comprehensive medical, dental, and vision, with additions like One Medical and Spring Health and claims of 100% employer‑paid coverage for employees in some materials. This breadth indicates strong protection for employees and families.
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Wellbeing & Lifestyle Benefits — Everyday perks include a $10,000 annual learning budget, daily catered meals, commuter support, fitness memberships (e.g., Equinox), and quarterly offsites. Public materials also cite significant per‑employee investment beyond salary.
Vapi Insights
What We Do
Vapi lets enterprises deploy human-like voice agents in minutes. Whether you’re building a voice product or trying to handle millions of calls, Vapi’s reliable infrastructure and flexible APIs make it easy. Everyone from YC startups to Fortune 500 companies rely on Vapi because it is: Flexible: Plug in your APIs, your customer data, your models Scalable: Handle millions of calls with <500ms latency Secure: LLM guardrails, HIPAA, SOC-2 Helping enterprises over this challenge, Vapi, has raised $20 million in Series A funding, led by Bessemer Venture Partners with participation from Abstract Ventures, AI Grant, Y Combinator, Saga Ventures, and Michael Ovitz.
