Get Data Engineers
Get Data Engineers from Latin America
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.
Why LATAM for data engineering
40-50% cost savings
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.
Data-intensive industry experience
LATAM's fintech, e-commerce, and agricultural sectors generate significant data volumes. Engineers here have worked on pipelines processing millions of events daily.
Strong CS and statistics foundations
Data engineering requires understanding distributed systems, storage formats, and query optimization. LATAM CS programs build that foundation.
Compatible timezones
4-8 hours of overlap with US teams. Data engineering work often involves cross-functional coordination with analytics, product, and ML teams.
Modern data stack adoption
dbt, Airflow, Spark, and Snowflake are all active in LATAM's data engineering community. Not just legacy ETL tools.
What our data engineers know
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)How we vet data engineers
Every candidate completes all seven stages before you see their profile. You can also run your own technical round after our screening.
* For common roles like full-stack. Other roles typically take 7 to 10 days.
CV check
Stage 1We review experience, stack, seniority, and career history against the role before anyone is invited to interview. Most applicants stop here.
Behavioral interview
Stage 2A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
Technical interview 1
Role-specificA 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.
Technical interview 2
Stage 4A second, deeper round on system design, architecture tradeoffs, and code review, matched to the seniority you are hiring for.
Take-home assessment
Stage 5A practical assignment that mirrors real work. We review code quality, testing, documentation, and how the candidate explains their decisions.
Reference check
Stage 6We speak with at least two professional references who worked with the candidate, not personal contacts.
Background check
Stage 7Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Who you might hire
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
Senior Data Engineer
São Paulo, Brazil
6+ years experience
Mid Data Engineer
Buenos Aires, Argentina
3+ years experience
Lead Data Engineer
Curitiba, Brazil
8+ years experience
Want to see profiles matched to your specific requirements?
Request matched profiles
What to expect
What sets our data engineers apart
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.
LATAM vs US data engineer salaries
Junior Data Engineer
Mid Data Engineer
Senior Data Engineer
FAQ
Common questions
Ready to meet your data engineers?
Tell us your data stack and team size. We'll have a shortlist ready in 3 days.