Data Lead

# Data LeadWonderschoolSan FranciscoFull-timeAI tools:Claude CodeHermesOpenClawUpload your resume once and Scout applies for youApply with ScoutShare this job:LinkedInXSave imageBuild the company-wide data foundation for Wonderschool’s childcare products, government platform, and AI agents. You’ll own provider and family data models, state data integrations, and data quality, working hands-on and reporting to the CEO.* Permanent* On-site### Skills and expertiseSQLBigQuerydbtHubSpotStripeClaude CodeHermesOpenClaw### Key responsibilities* Build a trusted canonical data model across company products, systems, and government platforms.* Own state data integrations, historical migrations, validation, and governance sign-off.* Define business metrics and establish monitoring, lineage, and data quality standards.## How this job comparesCompared with 43 open lead data jobs on October 10, 2026.Pay: This job does not publish its pay. The 11 open lead data jobs that publish US pay start at $174K a year on average.Similar open jobs: We Love AI Jobs lists 43 open lead data jobs at 29 companies.Remote work: This job is on site. 14 of the 43 lead data jobs (33%) are remote.AI in this role: Medium. The role uses AI tools and works with AI systems as a regular part of the job. The core role is data modeling and government data delivery, rather than building AI models or products. It uses AI agents heavily and structures data for agents that support provider coaching.## Full descriptionAbout WonderschoolWonderschool'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 RoleWe 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 questionsWhat Success Looks Like• State data loads pass validation and acceptance without heroics• Zero duplicate-provider incidents across our systems and the state's• Anyone in the company can answer \"how is this provider doing\" in one query, and so can an agent• Agents deliver accurate, personalized coaching to a pilot cohort of providers, validated by our provider success team• Metrics stop being debated in meetings because definitions are documented and trustedWho You AreYou have been the first or early data hire at a startup and you have also shipped data work in a regulated environment, govtech, healthtech, or fintech, where governance, masking, and audit trails are table stakes. You are as comfortable writing a dbt model as you are working through a data governance agreement with a state counterpart. You have real scar tissue from entity resolution and dedup work. You already use AI agents as a daily part of how you work, you understand what makes data legible to an LLM, and you figure things out independently. You are happiest as a team of one or two and would rather automate a task than hire around it. You want to build the system, not inherit one.Location #J-18808-Ljbffr