Head of Data
AIOS $150K - $300K/year Posted about 2 hours ago
About the role
About AIOS
AIOS is building the world’s first full-stack AI doctor.
We’re at $350M ARR, growing from $10M/yr 12 months ago. We’re the world’s fastest growing AI doctor.
We’re faithfully serving >150k/mo patients via Bolt Pharmacy, our main UK brand. We’re profitable.
Being Head of Data at AIOS
We are building a world-class data team.
As Head of Data at AIOS, your fundamental role is to own the data stack end to end.
We’ve built strong foundations across our pipelines, dbt layer, and self-serve reporting (Hex). As AIOS has grown to more than $350M in annualized revenue and >150k active patients, the importance and complexity of data across the company has grown with it.
Key responsibilities
- Stack: You’ll be the DRI for data across every AIOS brand and market, including ingestion, Redshift, dbt, Hex, product-event tracking, integrations, data quality, monitoring, and access.
- Architecture: You’ll define how our data platform should evolve as AIOS grows by orders of magnitude. You’ll make the important architectural decisions while working through our Analytics Engineering Lead and the rest of the team to execute them.
- dbt: You’ll establish clear standards for staging, intermediate, and marts models. You’ll define how new models are proposed, designed, reviewed, tested, documented, deployed, and eventually deprecated.
- Metrics: You’ll own the definitions behind the metrics that run AIOS across growth, activation, retention, revenue, product, clinical operations, CX, and Ops. You’ll eliminate conflicting definitions and turn core metrics into trusted company infrastructure.
- Hex AI: You’ll make Hex AI the best possible interface for understanding the business. Leaders should be able to ask important questions against clean, intuitive models without recreating business logic in every dashboard.
- Performance & Reliability: You’ll work with the team to keep our warehouse and pipelines fast, reliable, and cost-efficient as data volume and usage grow. You’ll set expectations for performance, freshness, monitoring, incident response, and cost.
- Experimentation: You’ll establish how AIOS designs, instruments, and evaluates experiments. You’ll ensure results are statistically sound, prevent teams from drawing confident conclusions from weak evidence, and identify when specialist Data Science support is needed.
- Engineering: You’ll partner closely with Engineering on source schemas, product events, breaking changes, data contracts, and backfills. Product changes should flow predictably into trustworthy analytical data.
- Business Partner: You’ll become a key partner to every functional lead. You’ll understand their domain deeply, challenge weak KPIs, and help them find the measurements that actually drive better decisions.
- Build the Team: You’ll lead our existing data team, hire an additional Data Engineer once you’re onboarded, and continue growing the function based on what the company needs most.
- Regulatory Evidence: You’ll start designing the data foundations required to demonstrate the quality, safety, and performance of our clinical and AI systems. When we talk to regulators, we should be fully data-backed.
- Vendors: You’ll own our relationships with Hex and other important data vendors. You’ll lead escalations, push for reliable products, evaluate whether tools still serve us, and negotiate as AIOS scales.
- Systems: You’ll move important business logic out of ad hoc dashboards and into governed models. When a problem repeats, you’ll turn the solution into reusable scaffolding rather than fixing it for the fifth time.
Need to have
- Experience: You have 5+ years as a hands-on data IC and 3+ years leading teams, including directly managing at least a couple of people. Most of your career has been spent close to the metal.
- SQL & dbt: You’re deeply fluent in SQL and dbt. You can read complex transformation logic, identify grain and join problems, review a marts-layer design, and quickly work out why two reports disagree.
- Data Architecture: You’ve helped design and operate a modern data platform spanning ingestion, a cloud warehouse, transformation, reporting, and monitoring. You understand how each layer should fit together.
- Data Engineering: You understand how production data systems behave beyond the modeling layer. You can reason about orchestration, incremental processing, dependencies, backfills, failure recovery, freshness, and data-quality controls
- Data Modelling: You know how to design models that remain clean and understandable as the business changes. You care about contracts, tests, lineage, ownership, documentation, and intuitive interfaces.
- Metrics: You can turn an ambiguous business goal into a precise metric. You deeply understand funnels, cohorts, activation, retention, churn, revenue, and growth.
- AI-Native: You use AI heavily in your own work and have strong instincts for making AI-assisted analysis more reliable, contextual, and useful.
- Judgement: You know which logic belongs in dbt, which belongs in Hex, which requests should become canonical models, and which requests should not be built at all.
- Leadership: You set a crisp direction and quality bar while giving strong people room to execute. You know how to develop an existing team while building the broader function around it.
- Player-Coach: You’re excited to primarily operate at the managerial and system level, but you haven’t lost the ability or willingness to inspect the actual work. You won’t hide behind your team.
- Stakeholders: You challenge senior leaders without hesitation when a KPI is misleading or poorly defined, then work with them to build a measure that drives better decisions.
- Reliability: You’ve established monitoring, data-quality checks, ownership, and incident processes for business-critical data. You don’t accept “the dashboard sometimes doesn’t update” as an enduring fact of life.
- Ownership: When a model is wrong, a dashboard is stale, or a vendor is failing us, you take responsibility for reaching the outcome. You do not stop at identifying which system or person is technically at fault.
Nice to have
- Scale: You’ve helped a data function keep pace with a company experiencing extreme growth. You enjoy the scary speed of a high-growth startup.
- Redshift: You’ve operated Redshift or a similar cloud data warehouse at meaningful scale.
- Hex: You’ve used Hex or another modern collaborative analytics platform and understand how to enable self-service without creating hundreds of competing truths.
- Consumer Metrics: You’ve worked deeply with acquisition, activation, retention, churn, revenue, cohorts, and unit economics in a consumer or subscription business.
- Multi-Market: You’ve built data systems supporting multiple brands, countries, and/or products without creating a separate data universe for each one.
- Regulatory Evidence: You’ve thought about how product and operational data can become traceable, auditable evidence for regulators or other high-stakes external stakeholders.
- Talent: You have a strong nose for exceptional data talent and know how to build a lean, high-performing team.
- Figure It Out: You can move from a broken executive dashboard, to a dbt architecture discussion, to a difficult vendor escalation, to a KPI debate with a functional lead while making progress on all four.
Benefits
- Healthcare: comprehensive medical insurance (if appropriate)
- Vacation: PTO with a yearly minimum (≥2wks/yr + local national holidays)
- Remote: our team is fully distributed across the world and functions fully remotely
- Personal development: budget for books, courses, coaching ($1200/yr)
- Personal wellness: budget for gym, health apps ($1200/yr)
- Coaching: free biweekly health coaching
- Equipment: Macbook & work-from-home equipment provided as needed
- What are we missing? We're still early so you get to shape our culture.
Skills
How to apply
Apply directly on the employer's application page. Your application goes straight to them.
Republished listing
This opportunity was discovered on AIOS's public careers page and is republished here for discovery purposes. Applications are handled by the employer.
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