AI 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 15d ago
The date the source published, not the day we noticed it (2026-09-25). Last seen at its source 3h ago.
We have tracked this listing since 5 Oct 2026 (5 days). The employer's own board has carried it every time we have read it, most recently 3 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Colombia
The description agrees: it names LATAM.
What the ad says
…Remote (…) LATAM…
Pay not stated
Similar roles pay $3,750–5,500/mo
Middle 50% of 17 listings that do state pay — Engineering · all levels · Colombia · USD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Ashby employer's own board first seen 5d ago · last seen 3h ago
The listing
AI Data Engineer
Katapult Labs · Remote (LATAM) · Full-time
About Katapult
Katapult is an AI-first engineering studio connecting senior LATAM talent with startups and companies in the US. We're not a staff-augmentation shop: every person we put in front of a client represents Katapult, co-creates product, and owns the outcome directly, with no managers or leads acting as a layer in between.
The Opportunity
We're looking for a Data Engineer who builds, not just maintains. You'll work with one of our US startup partners to create the data foundation behind their product and business decisions. That means bringing scattered sources into one trusted place, keeping the data flowing reliably, and turning it into insights the team can act on.
This isn't a back-office pipelines role. You'll work side by side with product and business leaders. You'll need to understand what the company is trying to achieve and why each dataset matters. Then you'll use that context to decide what to build next.
What You'll Do
Design, build, and own ETL/ELT pipelines that bring data from multiple sources (APIs, SaaS tools, CRMs, transactional databases) into Snowflake.
Model data in Snowflake so it's clean, trustworthy, and easy to use for analytics and product.
Build and maintain integrations across the stack. You'll decide when to use off-the-shelf connectors and when to write custom code.
Work directly with product and business leaders to turn vague questions into concrete data solutions.
Use AI tools and agents to speed up your own work and to automate manual workflows for the team.
Take ownership end to end: spot the problem, propose the solution, ship it, and keep improving it.
What We're Looking For
Hands-on data engineering experience. You've built production pipelines yourself, in real code (Python and SQL), not only through dashboards or notebooks.
Strong Snowflake experience, including data modeling, performance, and cost awareness.
Experience integrating multiple data sources and orchestrating pipelines (Airflow, dbt, Fivetran/Airbyte, or similar).
Agentic . Experience building AI agents or LLM-powered workflows on top of company data.
Business and product sense. You can talk with non-technical stakeholders, understand the business model, and prioritize by impact.
Impeccable English, spoken and written. You'll work directly with a US team daily.
Comfortable in an early-stage, ambiguous environment where you're a builder, not a ticket-taker.
Nice to Have
Background in startups or a founder/co-founder experience.
Familiarity with CI/CD, testing, and version control practices for data (Git, dbt tests, data quality checks).
Cloud experience (AWS, GCP, or Azure).