Member of the Technical Staff - Data Engineer

Stand Insurance•San Francisco, CA

3d•$240,000 - $295,000•Onsite

About The Position

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.

Requirements

Proven experience migrating a live analytics store onto a warehouse in production, including cutover and parity proof.
Experience with dbt in production, including incremental models, snapshots, and tests.
Hands-on experience with a real orchestrator (Dagster, Airflow, Prefect) including dependency graphs, backfills, and retries.
Track record of building data-quality or observability infrastructure (reconciliation suites, freshness monitoring, lineage, alerting).
Proficiency designing schemas and metric definitions that non-engineers can use.
Familiarity with authorization and access-control patterns for data.
Comfort and experience in product discovery, working directly with stakeholders.
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.

Nice To Haves

Insurance data experience (Policy administration systems, 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.
Curating data for ML and evals (training sets, labeled decision logs, feature stores, keeping a holdout honest).
Reverse ETL (pushing modeled data back into operational surfaces).
Early-stage experience where you were the first data hire and had to choose what not to build.

Responsibilities

Migrate the current centralized analytics database in Postgres to a new warehouse solution.
Implement dbt for data modeling.
Set up a real orchestrator for scheduling data pipelines.
Establish per-dataset cadence and freshness SLAs.
Implement snapshots on key entities.
Develop a trust framework and diagnostics console for data reconciliation and monitoring.
Create a shared semantic layer with a registry of typed, versioned, permission-aware entities and metrics.
Build an automated production to sanitized pipeline for de-identified data.
Partner with Actuarial, Underwriting, and Applied Science teams to understand data needs and build solutions.
Ensure data quality, freshness, and reliability.