This is a remote role for candidates based in the United States. Fundable's founders are in New York, so New York-based candidates are welcome, but living in New York is not required.

Fundable is building a data platform that transforms information from dozens of sources into reliable, up-to-date company intelligence. As Senior Data Engineer, you'll own this platform end-to-end—from architecture and ingestion through production operation—turning high-volume data from APIs, webhooks, files, and web scraping into trusted records that power our products.

What You'll Do

• Design and build a multi-source data platform supporting concurrent ingestion, event-driven processing, and orchestrated batch workflows.
• Implement concurrent intake for provider APIs, webhooks, files, and scraping with rate limiting and robust failure handling.
• Orchestrate scheduled workflows and historical backfills using tools like Airflow, Dagster, or Prefect.
• Build event-driven systems with Pub/Sub or Cloud Tasks, including retries, dead-letter queues, and controlled replay.
• Preserve original payloads in Google Cloud Storage before transforming them with dbt into medallion layers (raw, standardized, curated).
• Implement idempotent upserts and safe retries that prevent duplicate records and data corruption.
• Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures.
• Design and optimize PostgreSQL schemas and loading patterns for reliable downstream consumption.
• Integrate provider enrichment and LLM-based structured extraction into the platform.

Must-have experience

• 5+ years building and operating multi-source production data platforms, with ownership from architecture and implementation through deployment and ongoing operation.
• Production workflow orchestration with Airflow, Dagster, Prefect, Temporal, or a comparable tool, including dependencies, retries, and failure recovery. Airflow experience is preferred.
• Hands-on production experience with dbt and Google Cloud Platform.
• Event-driven systems with messaging or task queues, asynchronous processing, and failure recovery.
• Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading.
• Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
• Data contracts, raw and curated storage, retention policies, automated validation and pipeline tests, and monitoring for freshness and failures.

Nice-to-have experience

• 8+ years building production data platforms.
• Building a platform or substantial pipelines from zero to one with broad ownership in an early-stage team or independent project, rather than only maintaining established systems.
• Hands-on web scraping with tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrency, rate limits, and failure handling. Scraping will be a significant part of this job, but prior experience is not a dealbreaker.
• Startup or small-team experience with broad ownership and changing priorities.
• Entity resolution: matching company and people records across sources and reconciling duplicates.
• LLM-based structured extraction or company and people data enrichment.
• Familiarity with BigQuery and Dataform.

There is no required working-hours overlap.