Silver

Fundable - Senior Data Engineer

Argentina

Live, first seen by us 15 days ago

Pay: Pay not listed
Work setting: Remote — work setting source
Location type: Remote
From the employer’s posting
Employment: Contract — employment source
Employment type: Contract
From the employer’s posting
Team: Engineering — team source
Department: Engineering
From the employer’s posting

What you’ll work on

Full posting

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.

Build concurrent ingestion for provider APIs, webhooks, files and scraping
Orchestrate scheduled workflows and historical backfills in Airflow, Dagster or Prefect
Build event-driven intake with retries, dead-letter queues and controlled replay
Preserve raw payloads in GCS and transform them with dbt into medallion layers
Implement idempotent upserts and safe retries and backfills without duplicates

From the employer’s posting

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.

Build concurrent ingestion for provider APIs, webhooks, files, and scraping

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

Preserve original payloads, including provider JSON, in Google Cloud Storage (GCS) before transforming them with dbt

Implement idempotent upserts and safe retries, replay, and historical backfills that avoid duplicate records or data corruption

About Silver

Fundable is a startup data platform that helps investors and other teams discover and research companies.

In the employer’s words · Read in context

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Posting record

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Status in our records: Live
First seen by us: Oct 20, 202615 days ago
Recorded sightings: 19
Last seen by us: Nov 4, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

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Day 15

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From 10,736 similar postings that came down. Coming down is not always a hire.

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1 open job

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See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

Python
SQL
BigQuery
dbt
GCP
Google Cloud Storage
PostgreSQL
Prefect
Airflow
Dagster

Where the posting names them

- 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.

- 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.

- LLM-based structured extraction or company and people data enrichment. - Familiarity with BigQuery and Dataform. Interview Process

- 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.

- 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.

- 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.

- 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.

- 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.

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About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025: $139,500
Expected change, 2025 to 2035: 9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

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

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

Source details

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