A senior data engineer in Latin America costs about $8,500 a month at Plugg's benchmark, against about $24,000 a month for a US hire, a difference of $15,500 a month. Plugg sends a vetted shortlist in about 3 days, and your engineer works US hours.
What a data engineer costs: US vs Latin America
| Experience | US monthly median cost | Latin America monthly median cost | Difference |
|---|---|---|---|
| Senior | $24,000 | $8,500 | $15,500 a month (65% less) |
How we calculated these numbers
What the figures are. Monthly cost for a senior-level hire, in US dollars. The US column is what the employer pays each month for a US hire, and the Latin America column is what a comparable hire in Latin America costs each month. The figures come from Plugg Technologies.
Difference. We subtract the Latin America figure from the US figure. Multiply it by 12 for a rough yearly saving.
These figures are illustrative, based on typical senior-level ranges, and are not a quote. Your actual number depends on the role, seniority, stack and country. Request developer rates for a real number.
Want real numbers for your role?
Share the role, the stack and your hours. You get 3 to 5 vetted candidates and a real rate, with no upfront fees.
Request Developer RatesDashboards, reports and machine learning features are only as good as the data under them, and data engineers are the people who keep that layer trustworthy. This page covers what they cost in Latin America, what to screen for, and where to hire them.
What a data engineer does
A data engineer moves data from where it is created to where it can be used. That means pulling it from product databases, SaaS tools and event streams, cleaning and modeling it, loading it into a warehouse, and keeping the pipelines running on schedule. On a US team they work with analysts and data scientists who query the data, and with backend developers who produce it.
The work is quieter than other engineering roles and the failures are loud: a broken pipeline means a wrong number in front of the executive team. Look for people who care about tests, lineage and alerting on their own pipelines, and not only about getting data to land.
Skills and stack to look for
- Languages: SQL at a high level, plus Python (and Scala or Java where Spark is in use)
- Warehouses and lakes: Snowflake, BigQuery, Redshift or Databricks, and an opinion on partitioning and cost
- Transformation: dbt, with tests, documentation and a sensible model layout
- Orchestration: Airflow, Dagster or Prefect, with retries, backfills and alerts
- Streaming and scale: Kafka or Kinesis, and Spark for large batch jobs
- Ingestion and quality: Fivetran or Airbyte, data contracts, Great Expectations or similar checks
- Cloud basics: AWS, GCP or Azure, IAM and cost control
What affects the rate
Seniority and stack depth move the number more than anything else. An engineer who has designed a warehouse from scratch costs more than one who maintains existing dbt models, and streaming experience adds a premium. English level and the country also matter. The figures in the table above are senior-level monthly benchmarks from Plugg, shared with the AI and ML role. Treat the difference as a rough guide and ask for a quote for your exact role.
How Plugg screens, and what to test yourself
Plugg screens candidates against your hiring bar and for advanced-level English communication before you see a profile, and has also helped clients with technical vetting. You then interview the shortlist and make the final decision.
For a data role, test SQL and judgment together. Give the candidate a messy sample dataset and ask them to describe how they would model it, then have them write a query that answers a business question and explain what could make the answer wrong. Ask how they would find out that yesterday's load failed and how they would backfill it. Strong candidates talk about tests and monitoring without being prompted, and they ask who uses the data before they pick a tool.
When to hire a data engineer
- Analysts spend their week cleaning exports and rebuilding the same spreadsheet
- Reports disagree because every team defines a metric differently
- You are moving off a single reporting database to a warehouse
- You want to ship ML or AI features and the underlying data is not ready
Where to hire data engineers
Data work is less tied to the clock than front-end or support work, but pipeline failures still need a quick response, so overlap with your team helps. Colombia and Mexico sit on or near US Central time, and Argentina and Brazil have deep engineering communities and overlap with a US Eastern afternoon. If you need people to build the services that produce the data, see backend developers, and for the infrastructure underneath, see DevOps engineers.