Data engineer

This is not a single opening. When a US company asks for this skill, we introduce engineers from the network. Active client openings are on LinkedIn.

What this work is

You build pipelines a US team can schedule and trust. A pipeline feeds a report or a product feature. It fails a check when the data is wrong, instead of quietly shipping a bad number.

The stack

• SQL
• The warehouse the team uses
• Airflow, dbt, or Spark, matching the stack
• Tests or freshness checks
• The scheduler
• The report or feature that reads the output

What we look for

• A pipeline that feeds a report or a product feature
• SQL and a warehouse the team already uses
• Tests or checks that fail when the data is wrong
• 5 or more years in data or software engineering

How we check

We review work you have shipped. A keyword on a resume is not enough. You need advanced or fluent English. You will be in standups, code reviews, and Slack with a US team. You use AI tools to draft a model or a SQL check, then you compare the output to the source before it is scheduled. Every engineer is assessed on the four-level AI fluency scale before a client meets them. See the four levels.

How a placement runs

You join the client team and work in their repo, their standups, and their code reviews. You work US business hours from Mexico, Costa Rica, Colombia, Peru, Brazil, Chile, Argentina, or Uruguay. The placement is almost always a full-time contract. You are paid the same amount every month in US dollars. The monthly invoice is for bookkeeping. Candidates never pay to apply or to join. How joining works.

Hiring data engineers.

Updated November 9, 2026.