What Is A Data Engineer?
A data engineer designs, builds, and maintains the data pipelines and storage systems that make data usable for everyone else. That means ingesting and transforming data from multiple sources, building reliable warehouses and schemas, and keeping batch and streaming jobs running so analysts, scientists, and other engineers can trust the data they’re working with.
- Also called
- Data Platform Engineer, ETL Engineer
- Reports to
- Head of Data / Engineering Manager
- Works on
- Pipelines, data models, warehousing
- Measured on
- Pipeline reliability, data quality
What A Data Engineer Actually Does
- 01 · PipelinesDesigns data pipelines that ingest, transform, and validate structured and unstructured data
- 02 · Data ModelingBuilds data models, schemas, and storage layers optimized for performance and cost
- 03 · Batch & StreamingImplements batch and streaming processing systems, scheduling jobs and monitoring data quality
- 04 · ReliabilityKeeps pipelines running and catches bad data before it reaches downstream dashboards and models
- 05 · CollaborationWorks with analysts, data scientists, and platform engineers to align on what the data needs to support
Data Engineer Vs. Similar Roles
| Role | Main focus | Primary metric |
|---|---|---|
| Data Engineer | Building and maintaining the pipelines and systems that make data usable | Pipeline reliability, data quality |
| Analytics Engineer | Modeling and testing data inside the warehouse for analysts to use | Model accuracy, documentation coverage |
| Data Analyst | Interpreting data to answer business questions and build reports | Report accuracy, turnaround time |
| Data Scientist | Building predictive models and running statistical experiments | Model accuracy, business impact |
A data engineer builds the pipelines and infrastructure; an analytics engineer models that data for analysis; a data analyst and data scientist are the ones actually using it to answer questions.
Skills To Screen For
Interview Questions Worth Asking
Hiring A Data Engineer In Latin America
How Much Of The Workday You’d Share
Bogota, Mexico City, Sao Paulo, and Buenos Aires don’t change their clocks, so the overlap shifts when yours does. Times shown are in your team’s time zone. National English-proficiency averages vary by market too: Argentina ranks in EF’s "High" band globally, while Colombia, Mexico, and Brazil sit in the "Low" band. Screen every candidate’s spoken English directly rather than assuming by country.
What It Costs
| Country | Entry level (1-3 yrs) | Senior (6+ yrs) |
|---|---|---|
| Colombia | $30K/yr | $74K/yr |
| Mexico | $34K/yr | $68K/yr |
| Brazil | $41K/yr | $78K/yr |
| Argentina | $34K/yr | $78K/yr |
The LatAm average above is a blended figure across experience levels, so senior-specific per-country pay can run above it, the same pattern as several other roles on this glossary.
Typical time from kickoff to signed offer. LatamCent delivers 3 to 5 candidate profiles in the first 10 days.
Frequently Asked Questions
A data analyst interprets data to answer business questions and build reports, and is measured on report accuracy and turnaround time. A data engineer builds and maintains the pipelines, schemas, and storage systems that make that data usable in the first place, and is measured on pipeline reliability and data quality. Analysts depend on engineers’ pipelines being solid before they can do their own work.
A data engineer focuses on the infrastructure layer: ingesting raw data, building pipelines, and designing the warehouse itself. An analytics engineer works a layer up, modeling and testing that data inside the warehouse so analysts can trust it. At smaller companies, one hire sometimes covers both; larger teams usually split the roles once the data volume and modeling workload justify it.
US data engineers average around $127,500 a year. Data engineers in Latin America average around $51,000 a year, about 60% less, saving roughly $76,500 a year per hire.
Most searches run 21 days from kickoff to a signed offer, with 3 to 5 candidate profiles delivered in the first 10 days.
No. A database administrator focuses on keeping existing databases running, secure, and performant. A data engineer builds the pipelines and systems that move and transform data across an organization, often working with multiple databases, warehouses, and streaming systems rather than administering a single one.
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