Data Lead

About Wonderschool Wonderschool's mission is to ensure every child has access to early education that helps them realize their full potential. We do this by helping providers - small business owners who run child care programs - start, operate, and grow their businesses with powerful software, coaching, and support. We also partner with governments to modernize the public child care system, including building the technology platform for a state agency responsible for licensing, subsidy, and provider data statewide. We are a Series B company backed by Andreessen Horowitz, Goldman Sachs, Long Journey Ventures, and First Round Capital. The business is cash flow positive and expanding that position. A core part of our strategy is using AI to automate how work gets done across the company. Our agents already operate across product, engineering, operations, and go-to-market. The bottleneck is no longer the agents. It is the data underneath them. About the Role We are hiring a Data Lead to build the data foundation the whole company runs on: our commercial product, our government platform, and the AI agents that coach and support providers at scale. This is a deeply hands-on role. You will write SQL, ship models, load data, and debug pipelines yourself every day. You will own two things that are really one problem: a single, trusted model of our providers, children, and families, and the delivery of state-mandated data work (Data Hub integration, legacy migration, identity resolution) for our government partners. You will use AI agents heavily to do this work. We run Claude Code, Hermes, and OpenClaw across the company, and we expect you to multiply your output with them. We stay small on purpose and hire only when the work demands it. Get this right and every agent, dashboard, and coaching interaction gets smarter. This role reports directly to the CEO. What You'll Do Build the canonical data model for providers, sites, children, and families across BigQuery, HubSpot, Stripe, and our product databases. One ID, one definition, one source of truth Structure our data so AI agents can read it and act on it: entity tables, snapshots, and pre-computed briefings that power provider coaching at scale Own data delivery for our state government platform: bi-directional Data Hub sync, historical licensing data migration, staging-to-prod loads, validation, and governance sign-off Lead identity resolution and deduplication across legacy state systems so every provider and child has one accurate record Define and document the metrics the business runs on, from "active provider" to churn risk, and make them self-serve for operations and provider success teams Set the data quality bar: monitoring, alerting, lineage, and handling of sensitive data in line with government agreements Use agents to automate your own workflows first, then help other teams do the same with their data questions What Success Looks Like State data loads pass validation and acceptance withou...