Head of Data

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.

We now need a data leader who can take ownership of the entire function: setting the technical direction, developing our team, governing company metrics, and ensuring every AIOS leader can make decisions using data they trust.

Your mandate is to turn our current foundation into the trusted data operating system for the entire company.

You’ll make sure:

• Our pipelines, models, and dashboards are reliable.
• Our dbt layer is clean, documented, and designed to scale.
• Every core company metric has a precise, canonical definition.
• Leaders can confidently use Hex AI to explore trusted data and answer important business questions.
• Engineering and Data have clear contracts for how product data enters the warehouse.
• We’re building the evidence layer required to speak credibly with regulators about our clinical and AI systems.

You’ll deeply understand every important metric across growth, activation, retention, revenue, product, clinical operations, CX, and Ops. When a functional lead proposes a KPI that doesn’t measure what they think it measures, you’ll tell them. You’ll then work with them until you land on something that does.

You’ll manage our existing Analytics Engineering Lead, hire an additional Data Engineer once you’re onboarded, and continue building the team around you as the company scales.

This is primarily a managerial role, but you’ll remain close to the metal. You can inspect a complex dbt model, challenge a marts-layer design, debug why a number looks wrong, and get your hands dirty when needed.

Great performance in this role means every AIOS leader trusts the data they use, our most important metrics live in governed models rather than scattered dashboard logic, and the data team operates as a single high-performance unit.

If you nail the role, you’ll have shaped the data culture, stack, and team of a company growing from $350M toward $1B in annual revenue.

This is a full-time, fully remote role, and you’ll work async in the timezone of your choice, as long as you’re around until midday Pacific Time for calls as needed.

This is a senior role. You’ll report directly to Gzim (VP of Engineering).

You’ll also work most closely with:

• Pedro Viana - Analytics Engineering Lead
• Richie Cartwright - CEO
• Functional leads across Growth, Product, Clinical, CX, Ops, and Finance

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.