What you’ll be doing

•    Building the data platform behind our product: ingestion, normalization, and enrichment pipelines for catalogs of millions of SKUs across Postgres and BigQuery, with lineage back to every source.

•    Syncing customer systems of record: mirroring data from customer PIMs, ERPs, and ecommerce platforms into BigQuery, with history. It’s millions of products and thousands of attributes.

•    Designing durable, long-running workflows: building research agents that run for hours across hundreds of LLM calls and recover cleanly when something fails.

•    Shipping APIs the product and customers depend on: versioned, multi-tenant APIs that power our web app and external integrations.

•    Owning infrastructure and on-call: keeping a data-intensive AI app fast, observable, and affordable. Includes the AI plumbing (caching, sandboxing, traces, evals) that lets us develop features with confidence.

•    Owning features end-to-end: shaping the idea, building an MVP, testing in production, and iterating. Backend-led, with frontend support when you need it.

•    Talking with and supporting customers: joining customer calls to ask questions and share early work, plus rotating into support.

Requirements

•    Strong software engineering fundamentals: you take a system from idea to deployed and stable.

•    Significant backend experience: production APIs, services, and data pipelines.

•    Experience with data engineering at scale: warehouses, ETL or ELT, batch and streaming patterns.

•    Comfort with distributed systems primitives: queues, workers, idempotency, retries, eventual consistency.

•    Experience designing APIs that other engineers or customers rely on day to day.

•    Comfort with our stack (TypeScript, Postgres, BigQuery) or transferable experience in similar tools.

•    Comfort operating in ambiguity and taking initiative without perfect specs.

•    Strong writing skills: you can make a complex idea clear in a few sentences.

•    Must be legally authorized to work in the US. We are unable to provide sponsorship for this role.

Nice to have

•    Worked with durable execution or workflow engines (Temporal, DBOS, Inngest, Restate, Airflow), or built one yourself.

•    Shipped or operated AI-powered features in production, including the observability and eval work that makes them trustworthy.

•    Big data tooling experience (BigQuery, Snowflake, large-scale ETL, columnar formats); bonus for warehouse transformation tooling like DBT, Dataform, or SQLMesh.

•    Multi-tenant SaaS experience, especially around isolation, auth, and tenant-aware data models.

•    Built or operated integrations with customer systems of record (ERPs, PIMs, ecommerce platforms).

•    Experience with OpenTelemetry, structured logging, and modern incident tooling (incident.io, PagerDuty, FireHydrant).

•    Experience at an early-stage SaaS startup or working closely with customers in a technical context.