Get Data Engineers
Data engineers who build reliable pipelines, maintain data warehouses, and create the infrastructure that analytics and ML teams depend on. LATAM has strong data engineering talent driven by the region's fintech and e-commerce sectors.
Senior data engineers in LATAM cost $70-100k vs $160-200k in the US. Data engineering is a specialized role commanding premium salaries, and LATAM provides meaningful cost relief.
LATAM's fintech, e-commerce, and agricultural sectors generate significant data volumes. Engineers here have worked on pipelines processing millions of events daily.
Data engineering requires understanding distributed systems, storage formats, and query optimization. LATAM CS programs build that foundation.
4-8 hours of overlap with US teams. Data engineering work often involves cross-functional coordination with analytics, product, and ML teams.
dbt, Airflow, Spark, and Snowflake are all active in LATAM's data engineering community. Not just legacy ETL tools.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 90-minute session covering pipeline design: candidates walk through how they'd build a pipeline from raw event data to an analytics-ready table, including idempotency, backfill strategy, and incremental loading. We cover dbt model design (staging vs intermediate vs mart layers, tests, documentation), and SQL optimization for a slow analytical query. We also discuss streaming vs batch tradeoffs for a given business requirement and how they'd approach data quality validation at each layer.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
6+ years experience
Buenos Aires, Argentina
3+ years experience
Curitiba, Brazil
8+ years experience
Want to see profiles matched to your specific requirements?
Request matched profilesWhat to expect
Data pipelines that work once aren't useful. Ours build pipelines that work reliably, fail gracefully, and can be reprocessed when something goes wrong upstream. They also document their work so the next engineer doesn't need to reverse-engineer it.
GitHub Copilot and AI tools for generating SQL transformations, dbt model code, and Airflow DAG boilerplate. They focus on data modeling decisions, not syntax.
AI-assisted data quality rule generation: using AI tools to suggest validation checks based on schema and expected value distributions.
They write dbt tests for every model that matters. Source freshness, schema tests, and custom SQL tests are part of their delivery checklist.
They document data pipelines as code: model descriptions, column-level lineage, and source documentation in the dbt project.
They treat pipeline failures as incidents. On-call documentation, runbooks, and alerting are part of their standard setup.
Junior Data Engineer
Mid Data Engineer
Senior Data Engineer
FAQ
Tell us your data stack and team size. We'll have a shortlist ready in 3 days.
Python 3.12+
Apache Airflow
Apache Spark
Kafka (streaming ingestion)
PostgreSQL (OLTP + analytical queries)
MongoDB
AWS S3 / GCS / Azure Data Lake
Docker
Kubernetes
Terraform (data infra)