Fundable - Senior Data Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 13d ago
The date the source published, not the day we noticed it (2026-10-19). Last seen at its source 1h ago.
We have tracked this listing since 19 Oct 2026 (13 days). The employer's own board has carried it every time we have read it, most recently 1 hour ago.
Is it remote?
Remote
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
Argentina
The description states no restriction of its own. This is the source's own tag.
Pay
$84k–120k/yr
Published by the source in its own salary field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 13d ago · last seen 1h ago
- location not stated
The listing
About Fundable
Fundable is a startup data platform that helps investors and other teams discover and research companies. We bring together information on companies, investors, funding activity, and people to make that research easier.
Role overview
We’re looking for a senior data engineer to own Fundable’s data platform from architecture through production, reliably turning high volumes of data from provider APIs, webhooks, files, and web scraping into trusted records for our products.
Today, our enrichment and extraction workflows largely run serially inside a container. You’ll lead the move to a platform that supports concurrent processing, event-driven intake, and orchestrated batch workflows.
What you’ll own
Multi-source intake: Build concurrent ingestion for provider APIs, webhooks, files, and scraping, with rate limits and failure handling.
Batch and event-driven execution: Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect. Build event-driven intake with Pub/Sub or Cloud Tasks, including retries, dead-letter queues, and controlled replay of failed work.
Raw-to-curated data: Preserve original payloads, including provider JSON, in Google Cloud Storage (GCS) before transforming them with dbt. Establish medallion layers for raw, standardized, and curated data.
Correctness through recovery: Implement idempotent upserts and safe retries, replay, and historical backfills that avoid duplicate records or data corruption. Make data traceable to its source and safe to reprocess.
Data quality and delivery: Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures. Design and optimize PostgreSQL schemas, queries, and loading patterns so downstream products receive dependable data.
Extraction and enrichment: Integrate provider enrichment and LLM-based structured extraction from text into the platform.
Must-have experience
Experience building multi-source production data platforms from zero to one, from initial architecture and pipeline implementation through deployment and ongoing operation.
Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal, including dependencies, retries, and failure recovery. Airflow experience is 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.
Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading. Experience with data contracts, raw and curated storage, retention policies, and automated quality checks.
Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
Hands-on web scraping experience with tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrent scraping, rate limits, and failure handling.
Nice-to-have experience
Experience working in startups or small engineering teams, with broad ownership and changing priorities.
Entity resolution experience is especially valuable: matching company and people records across sources and reconciling duplicates into consistent entities.
LLM-based structured extraction or company and people data enrichment.
Familiarity with BigQuery and Dataform.
Interview Process
Silver Screening Interview
Offline Case Study
Client Behavioral Interview
Client Case Study Interview
Client System Design Interview
Client Final Round Interview