Senior Data Engineer

About the role

Fundable is a startup data platform that helps investors and other teams discover and research companies by bringing together information on companies, investors, funding activity and people. It is looking for a senior data engineer to own the data platform from architecture through production, turning high volumes of data from provider APIs, webhooks, files and web scraping into trusted records. Today the enrichment and extraction workflows run serially in a container; the engineer will lead the move to a platform supporting concurrent processing, event-driven intake and orchestrated batch workflows.

Key responsibilities

• Build concurrent ingestion for provider APIs, webhooks, files and scraping, with rate limits and failure handling.
• Orchestrate scheduled workflows and historical backfills with tools such as Airflow, Dagster or Prefect, and build event-driven intake with Pub/Sub or Cloud Tasks, including retries, dead-letter queues and controlled replay of failed work.
• Store original payloads, including provider JSON, in Google Cloud Storage before transforming them with dbt, and establish medallion layers for raw, standardized and curated data.
• Implement idempotent upserts, safe retries, replay and backfills that avoid duplicate records, and make data traceable to its source.
• Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures; design and optimize PostgreSQL schemas, queries and loading patterns.
• Integrate provider enrichment and LLM-based structured extraction from text into the platform.

Required profile

• Experience building multi-source production data platforms from zero to one, from architecture and pipelines through deployment and operation.
• Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect or Temporal (Airflow preferred).
• Hands-on production experience with dbt and GCP.
• Experience building event-driven systems with messaging or task queues, including asynchronous processing and failure recovery.
• Nice to have: startup or small team experience, entity resolution, LLM-based extraction or enrichment, and familiarity with BigQuery and Dataform.

Required skills

• Strong SQL and PostgreSQL, including data modeling, query optimization and incremental loading.
• Strong Python, including async HTTP and concurrent processing with asyncio, aiohttp or HTTPX.
• Web scraping with Scrapy, Playwright, Selenium or Beautiful Soup.

The interview process includes a silver screening, an offline case study, client behavioral and case study interviews, and a final round.