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Key skills for this 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 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 trusted
Who You Are
You 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.
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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 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 trusted
Who You Are
You 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.
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