South helps growing companies find, hire, and pay top Latin American talent. Build high-performing teams in 21 days or less.
.svg){kind=logo}
A Data Engineer builds and maintains the systems that collect, transform, store, and deliver data.
They work behind the dashboards, reports, machine learning models, and business analyses that depend on accurate information.
Typical Data Engineer responsibilities include:
The exact responsibilities depend heavily on your data stack.
A Data Engineer at a growing SaaS company may spend most of their time working with Snowflake, dbt, Airflow, and Python.
Someone supporting high-volume event data may work more deeply with Kafka, Spark, Databricks, and distributed systems.
Hire for the data problems your company actually has rather than the longest possible list of tools.
A dedicated Data Engineer becomes valuable when collecting and preparing data starts consuming too much time or unreliable infrastructure begins limiting what the rest of the company can do.
Data Analysts should spend most of their time answering business questions.
If they're constantly:
you may be asking them to perform Data Engineering work.
A growing company can quickly accumulate data across:
A Data Engineer can centralize that information and create reliable pipelines between systems.
Finance says revenue is one number.
Sales reports another.
The executive dashboard shows a third.
This often indicates inconsistent definitions or unreliable transformation logic.
A Data Engineer can help create a more dependable data foundation.
Frequent failures, stale dashboards, incomplete loads, and manual restarts are signs that the data stack needs stronger engineering ownership.
A Data Engineer can introduce:
A warehouse implementation requires more than purchasing Snowflake or BigQuery.
Someone needs to determine:
A Data Engineer can own those decisions.
A few manually prepared dashboards may work early on.
As more departments need data, the organization needs reusable, documented models rather than separate calculations for every report.
Machine learning depends heavily on the quality of the underlying data.
A Data Engineer can build the pipelines, historical datasets, streaming infrastructure, and data-validation processes that ML systems require.
If your business needs live recommendations, alerts, fraud detection, operational analytics, or other low-latency workflows, you'll likely need stronger streaming expertise.
Data Scientists should focus heavily on analysis, modeling, experimentation, and ML.
If they're spending most of their time engineering data infrastructure, adding a dedicated Data Engineer can free them to focus on higher-value modeling work.
Strong Data Engineers combine software engineering, SQL, database knowledge, cloud systems, and production reliability.
SQL is one of the first skills I would evaluate.
Candidates should be comfortable with:
For warehouse-heavy environments, SQL depth matters substantially.
Python is common across modern data stacks.
Candidates may use it for:
Some environments may instead emphasize Java or Scala.
Candidates should understand how to build reliable pipelines rather than simply move data once.
Ask about:
Look for experience with the warehouse your team actually uses, such as:
Deep experience with one modern warehouse can be more valuable than shallow exposure to all of them.
Strong candidates should understand how raw data becomes business-ready information.
Evaluate experience with:
If your organization uses dbt, candidates should understand more than writing basic SQL models.
Look for experience with:
Candidates should be comfortable with tools such as:
They should understand scheduling, dependencies, retries, alerts, and backfills.
Ask how they know a pipeline produced the correct result.
Strong candidates should discuss testing and validation rather than assuming a successful job equals correct data.
Experience with AWS, Azure, or Google Cloud may include:
For real-time roles, evaluate experience with Kafka, Kinesis, Pub/Sub, Spark Streaming, or similar technologies.
Don't make streaming mandatory when your actual workloads are mostly batch.
Strong Data Engineers increasingly apply software-engineering practices to data.
Look for experience with:
Candidates should think about pipelines as production systems.
Ask how they detect:
Cloud data platforms make it easy to build expensive pipelines.
Strong senior candidates should be able to discuss how architecture, storage, compute, queries, and processing affect cost.
Data Engineers work closely with:
They need to understand what downstream users actually require rather than building infrastructure in isolation.
Data Engineer compensation varies by seniority, technical stack, cloud experience, and the scale of the data environment.
South's current Data Engineer benchmark lists an average U.S. salary of approximately $10,800 per month and an all-in monthly rate of approximately $5,425 for Latin American talent.
That's potential savings of around 50%.
The exact rate will depend on the engineer you need.
Junior Data Engineers can contribute to:
They work best when experienced data leadership and architecture already exist.
Mid-level engineers can generally own pipelines and data domains more independently.
They may:
Senior engineers can handle larger architectural decisions.
They may:
Some environments require deeper specialization.
Examples include:
The specialization should reflect the systems your company actually operates.
A Data Engineer interview should test SQL, pipeline design, data modeling, reliability, and architectural judgment.
Avoid making the entire interview a collection of definitions about Hadoop or Spark.
Use problems similar to the ones the person will solve after joining.
Ask:
Walk me through a data pipeline you built from source to final consumer.
Then ask:
Strong candidates should naturally discuss reliability and downstream users.
Give the candidate realistic tables and a business problem.
For example:
Using customers, subscriptions, and payments, calculate monthly recurring revenue by customer cohort.
Evaluate:
Ask:
Design a warehouse model for a SaaS business with accounts, users, subscriptions, plans, and payments.
Look for discussion around:
Ask:
An Airflow pipeline fails halfway through after loading 60% of yesterday's data. What should happen when you rerun it?
Strong candidates should discuss idempotency, checkpoints, retries, partial loads, and validation.
Ask:
You discover that one year of revenue data was transformed incorrectly. How would you correct it?
Look for a structured backfill plan that protects current production workloads and validates the result.
Ask:
Our Snowflake bill increased 60% last month. How would you investigate it?
Useful areas may include:
Ask:
When would you use streaming instead of batch processing?
Strong candidates should connect the architecture choice to latency requirements, complexity, cost, reliability, and business value.
Ask:
Your pipeline completed successfully, but a dashboard suddenly reports 30% fewer customers. What do you do?
This reveals whether the candidate understands that infrastructure health and data correctness are different things.
Useful questions include:
Choose questions that match your actual environment.
A Snowflake/dbt SaaS role doesn't need the same interview as a Kafka/Spark streaming position.
A Data Analyst primarily uses existing data to answer business questions.
A Data Engineer primarily builds the systems that make that analysis possible.
Hire a Data Engineer when you need:
Hire a Data Analyst when you need:
If your analyst can't answer questions because the underlying data is unreliable or unavailable, solve the Data Engineering problem first.
The distinction depends on the organization.
A Data Engineer generally works further upstream on:
An Analytics Engineer generally works closer to the analytical layer on:
A growing modern data team may eventually need both.
A Data Engineer builds the infrastructure and datasets.
A Data Scientist uses those datasets for:
If Data Scientists are spending most of their time repairing pipelines and preparing basic datasets, a Data Engineer can usually create more leverage.
South helps U.S. companies find Data Engineers across Latin America based on the specific architecture, stack, scale, and business requirements of the role.
Start with your current data problems.
Consider:
A Snowflake/dbt analytics stack and a high-volume streaming platform require very different profiles.
South identifies Data Engineers across Latin America whose backgrounds align with your requirements.
Evaluation can include:
You receive a focused selection of candidates instead of reviewing a large volume of generic engineering applications.
Compare their:
Your engineering or data team meets the candidates you want to consider.
Use realistic SQL exercises, pipeline scenarios, modeling questions, and discussions around your architecture to evaluate how they would perform in your environment.
You make the final hiring decision and manage your Data Engineer as part of your team.
South provides one consolidated monthly invoice covering your teammate's compensation and South's service.
There are no minimum commitments, and South offers a free replacement if you need to make a change.
Data Engineering involves more collaboration than the infrastructure itself might suggest.
Data Engineers regularly need to work with:
Latin America's overlap with U.S. business hours supports:
Companies can access Data Engineers across Latin America with experience in Snowflake, BigQuery, Databricks, AWS, Azure, GCP, dbt, Airflow, Spark, Kafka, and other modern data technologies.
A Data Engineer builds and maintains pipelines, warehouses, transformations, data models, orchestration, data-quality systems, and other infrastructure that makes reliable data available to analysts, applications, and machine learning systems.
Compensation depends on seniority and technical depth.
South's current calculator lists an all-in monthly rate of approximately $5,425 for Latin American Data Engineering talent compared with an average U.S. salary of around $10,800 per month.
Look for strong SQL, programming, ETL/ELT, data modeling, cloud warehouses, orchestration, testing, and production pipeline experience.
The exact technologies should match your data stack.
Python is extremely common in modern Data Engineering, although some environments rely more heavily on Java, Scala, or other languages.
SQL is also essential for many warehouse-focused positions.
Common tools include Snowflake, BigQuery, Redshift, Databricks, dbt, Airflow, Dagster, Prefect, Spark, Kafka, Fivetran, Airbyte, Python, SQL, and cloud platforms.
Prioritize the technologies your company actually uses.
A Data Engineer builds the infrastructure that delivers trustworthy data.
A Data Analyst uses that data to answer questions, build dashboards, investigate performance, and communicate business insights.
Consider hiring one when pipelines frequently fail, data is spread across disconnected systems, analysts spend too much time preparing data, reports disagree, you're building a warehouse, or machine learning teams need more reliable data infrastructure.
Yes. Latin America has Data Engineers with experience across cloud warehouses, modern ELT stacks, distributed processing, streaming, and data infrastructure, with working hours that can overlap closely with U.S. engineering teams.
The right Data Engineer gives your company reliable infrastructure instead of another collection of fragile scripts and disconnected datasets.
South helps U.S. companies find pre-vetted Data Engineers across Latin America based on SQL ability, programming skills, cloud experience, data architecture, production background, seniority, and communication.
Schedule a free call and find your next Data Engineer in Latin America with South.
The region has the perfect mix of everything you want in remote employees: English skills, shared time zones, hard-working, and depth of talent. They are already accustomed to working remotely for top US startups and Fortune 500 companies.
Absolutely! The US and Latin America have basically the same time zones. No Latin American city is more than two hours ahead of EST.
Every hire is sourced based on your exact needs. They will arrive ready to support your business right away. They can do basically any tasks done remotely, but we recommend starting them as support so your team has more bandwidth for high-value strategic tasks.
All types of roles - customer service, executive assistant, sales, accounting, email marketing, lead generation, content writers, operations, social media marketing, and more!
You can pay directly through us (most popular) or we can connect you with one of our payroll partners.
You don't have to deal with any American labor laws / taxes when hiring full-time remote contractors. They aren't US-based, so no visas or sponsorships to deal with either.
Pricing is one flat monthly rate per hire. The rate includes your teammate's compensation and South's service in a single consolidated invoice. It covers sourcing, vetting, payroll, compliance, ongoing support, and a free replacement if you ever need one. Rates vary by role and seniority. See our savings by role page for typical ranges, then we'll confirm an exact rate for your role.
There are no cancellation fees or minimum commitments, and you only pay if you make a hire.
Yes, we only recruit for full-time and we strongly recommend full-time hiring if you can. Stability (full-time & long-term) is highly sought after abroad. The top caliber candidates are only looking for full-time work.
You're also going to spend time training and getting them up to speed on your processes. It would be a waste to do that over and over again with new people all the time.
We recommend training new hires on one thing at a time.
For example, once they get up to speed on lead generation, you can add the next role writing blog posts or whatever you'd like. You can definitely overlap roles until you have enough work for multiple people.
The cost of living is much less in Latin American countries. Many of our employees are able to own homes, raise families, provide for their parents, and have in-home help of their own with their salaries.
If you aren't happy with your hire in the first 120 days, we will work with you to conduct a second round of search for the same role for free.
Just email us at Hello@HireInSouth.com and we will get back to you with an answer as soon as possible.
