Data Engineer

Why Join Stand: At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism. The real product is a scalable risk engine, our Stand World Model.

We stay when traditional insurers exit. We model what others approximate. And we build systems that change outcomes, not just prices.

Our leadership team has built category-defining companies (Metromile, Policygenius, WePay, HotelTonight) and brings decades of expertise in underwriting, capital management, and reinsurance.

Background: The property insurance industry is built to price loss after it happens. It relies on coarse proxies, backward-looking data, and manual processes, then accepts damage as unavoidable.

Stand takes a different approach. We simulate how real-world catastrophes affect individual properties, translate that into actionable decisions, and automate the business around it. The result is a platform that can underwrite what others can’t and operate with far less friction.

Role Summary

We are building the self-driving insurer: a national carrier whose standard operating procedure is explicit, instrumented, and handed to agents one proven step at a time — so the book grows without the org.

Every one of those decisions runs on data, and every one of them is only as good as the number underneath it. That's the precondition this role owns. Not a dashboard. The warehouse, the definitions, and the query surfaces that underwriting, pricing, and leadership all resolve to — and that our agents will read from long before they're trusted to write.

Concretely, the platform is four things:

A warehouse where every dataset moves at its own speed. Today we have a centralized analytics database in Postgres, rebuilt hourly in full. It works, and it's why you start on the second problem instead of the first. You migrate it: dbt for models, a real orchestrator for scheduling, per-dataset cadence and freshness SLAs, snapshots on the entities whose history matters. Bind state on minutes, vendor pulls and reference data daily.

A trust framework and diagnostics console. Reconciliation between each system of record and the warehouse — row counts, premium totals, status parity — running as tests on every load and failing the run, not as a monthly spot check. A console where anyone, not just you, can see whether a dataset is fresh, whether it reconciled, and which dashboards are affected when it didn't. Turning a discrepancy someone noticed into a permanent test should take an afternoon.

A shared semantic layer. One registry of typed, versioned, permission-aware entities and metrics. A metric means one thing, defined once, in version control. Deciding what a metric should mean isn't your call; making it easy for an actuary or underwriter to codify it once, so the next person doesn't redefine it in a dashboard, is your job. The agentic query surface is built on this layer and can't see past it: an agent answers in natural language, shows the query it ran, and reaches nothing its invoking user couldn't reach directly.

Data that's safe to be creative with. An automated production → sanitized pipeline producing a de-identified but faithful copy of the book — consistent synthetic identities, preserved distributions, intact joins, edge cases kept rather than smoothed away. It becomes the default seed for local, dev, and staging, and the default substrate for anyone prototyping against real-shaped data. Raw PII access becomes the exception with an audit trail.

You'll partner closely with Actuarial and Underwriting, who ask the questions, and with Applied Science, whose models both consume this data and produce more of it. Working to understand what insights are missing and build a pathway to get them the information they need. You start by understanding their problem, then you solve it, and then you build a harness that can help them solve problems themselves

This is a foundations role. Your success is measured by the questions other people answer without you: how fast an actuary can rate a cohort, how confidently an underwriter can trust a stage count, and how quickly an engineer can prototype against realistic data without filing a request.

What you'll ship

• First 30 days: Warehouse chosen and justified in writing — cost at our volume, latency, how it reads from operational Postgres, who administers it. A dbt project that reproduces today's tables, with an automated parity check against the existing store that passes before anything changes. Orchestrator running the current schedule unchanged, now with a dependency graph and per-model run history.

• First 3 months: A governance-first pipeline that empowers stakeholders to self-serve metric definitions and targets, independently promoting ideas to productionized, tested models. Launch of the diagnostics console, enabling non-engineers to monitor data health, freshness SLAs, and reconciliation status without engineering intervention.

• First 6 months: A semantic layer that dashboards, analysts, and agents all resolve to, with permissions enforced centrally and covered by tests. A sanitized production mirror refreshing on schedule and seeding every non-production environment. Leadership pipeline, loss ratio, and premium reporting all coming from one source, produced without engineering in the loop.

Core competencies (Required)

• Min of 12 years of experience in industry

• Proven experience migrating a live analytics store onto a warehouse in production — including the cutover and the parity proof, not a greenfield build.

• dbt in production: incremental models, snapshots, tests, and the operational habits around them.

• Hands-on experience with a real orchestrator (Dagster, Airflow, Prefect) — dependency graphs, backfills, retries, and being paged when a load fails.

• Track record of building data-quality or observability infrastructure: reconciliation suites, freshness monitoring, lineage, alerting people didn't turn off.

• Proficiency designing schemas and metric definitions that non-engineers can actually use.

• Familiarity with authorization and access-control patterns for data.

• Comfort and experience in product discovery — working directly with the people who need the answer, not from a ticket.

Nice-to-have skills

• Insurance data experience. Policy administration systems (Socotra, Guidewire, Duck Creek), written vs. earned premium, loss and premium triangles, reserving and development, statutory or bureau reporting, or having partnered closely with actuaries.

• Geospatial data at scale — parcels, hazard layers, imagery, point clouds — with PostGIS, H3, or similar. Our unit of analysis is a specific building on a specific parcel.

• Curating data for ML and evals — training sets, labeled decision logs, feature stores, and the discipline of keeping a holdout honest.

• Reverse ETL — pushing modeled data back into the operational surfaces where people actually work.

• Early-stage experience where you were the first data hire and had to choose what not to build.

You're not a fit if

• You have less than 12 years of experience in industry

• Your data experience is primarily building dashboards on top of models someone else owned.

• You want a fully specified backlog. This role is defined by ambiguity for the first year.

• You'd rather design the ideal warehouse than migrate the working one, or you'd leave the old pipeline running indefinitely because removing it feels risky.

The annual base salary range for full-time employees in this position is $240,000 to $295,000 + meaningful Equity Grant.

Compensation decisions are dependent on several factors including, but not limited to, an individual’s qualifications, location where the role is to be performed, internal equity, and alignment with market data.

Benefits:

• Above-market Health, Dental, and Vision coverage

• Weekly lunch stipend

• Flexible time off + holidays

• 401(k) plan

• Commuter benefits

• PAT & MAT Leave

• Short-Term and Long-Term Disability

• Monthly team gatherings

• In-office perks

AI in the Interview Process

Thoughtful use of AI tools is expected and valued at Stand. Candidates should be prepared to discuss how they use AI, how they evaluate its output, and how it informs their work. Strong communication, sound judgment, and the ability to clearly articulate experience and decision-making remain core requirements.

Some parts of the interview process are designed to assess independent thinking, communication, and problem-solving. If you plan to use an AI assistant or LLM during any portion of an interview, please discuss it with your interviewer in advance.

Work Authorization

Candidates must be authorized to work in the U.S. Stand does not sponsor new work visas. We can consider candidates on TN visas, O-1A visas, or H-1B transfers with three years or more remaining.

Equal Opportunity Employment

Stand is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. We believe that diversity enriches the workplace, and we are committed to growing our team with the most talented and passionate people from every community.

We are committed to providing reasonable accommodations for qualified individuals. If you require assistance, please contact us at [email protected].

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

AI Use in Hiring

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us at [email protected].