🧠 Data Engineer (Integrations & Data Quality)

🌍 Location & Work Setup
🌎 Remote – Latin America
💼 Full-time | B2B Contractor
⏰ 7 working hours/day
🌴 20 PTO days/year + national holidays + 3 sick days
🏢 About the Role
We're looking for a mid/senior Data Engineer to join a fast-growing tech company that builds data products for online retail brands.
The focus is pipelines, transformations, and data quality, with no data science or ML work. Connecting to an API is the easy part. The real job is figuring out why a new vendor's data looks wrong before modeling it.
A great fit for a curious, independent "data detective" who ships fast and works comfortably with AI coding agents.
🔍 Responsibilities
Build ingestion pipelines from third-party APIs and vendor systems
Investigate new data sources: spot nulls, inconsistent types, and odd behaviors, and find the root cause
Model and test data in dbt so it's clean and trustworthy
Direct AI coding agents and critically review the code they produce
Ship new integrations within tight customer timelines and document each source's quirks
🛠️ Requirements
5+ years of experience in data engineering
Strong Python and SQL
Hands-on experience with dbt
Pipelines built from third-party APIs, ideally with dlt (Fivetran or Airbyte also relevant)
Experience untangling messy, poorly documented source data
Day-to-day use of AI coding agents
✨ Nice to Have
E-commerce or DTC data (store platforms, ad platforms, marketing tools)
DuckDB or MotherDuck
Temporal or similar orchestration tools
🧠 Who You Are
Curious and patient: you dig until you understand why the data looks wrong
Independent: you pick up new systems quickly, without hand-holding
Comfortable with startup pace, without compromising on quality
A clear communicator about data issues and their impact
🎓 Education
Degree in Computer Science, Engineering, or a related field (or equivalent experience)
Strong spoken and written English

Work arrangement
Yes