South helps growing companies find, hire, and pay top Latin American talent. Build high-performing teams in 21 days or less.
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A dbt Developer builds and maintains the transformation layer between raw warehouse data and the datasets consumed by analytics, BI, applications, and other data products.
Their work typically combines:
Common responsibilities include:
The exact role depends heavily on your data stack.
A dbt Developer working on Snowflake might spend considerable time optimizing:
A BigQuery-focused developer may pay more attention to:
A developer joining a mature data organization may work more heavily with:
Hire for the dbt environment you actually operate rather than treating dbt as a generic SQL keyword on the job description.
One dashboard says revenue is $4.2 million.
Another says $4.5 million.
A finance report shows $4.3 million.
The problem may be that every downstream consumer is implementing the metric independently.
A dbt Developer can move shared business logic upstream into governed transformation models.
Your transformations may currently exist across:
As the number of transformations grows, it becomes harder to understand:
A dbt Developer can refactor those transformations into a structured project.
You've already invested in:
and your source data is landing reliably.
The next challenge is turning raw information into trusted data products.
That's where a dbt specialist becomes especially useful.
Analysts should be able to spend more time analyzing the business and less time repeatedly:
A dbt Developer can move common cleanup and business logic into reusable upstream models.
Small dbt projects can grow surprisingly quickly.
Signs of trouble include:
A senior dbt Developer can restructure the project before technical debt slows the entire analytics team.
Users may report problems such as:
A dbt Developer can introduce stronger:
around the transformation layer.
As the data stack grows, teams need to know:
What breaks if I change this model?
A well-structured dbt graph can make dependencies much more visible.
Large dbt projects can consume substantial warehouse compute.
Problems may include:
A strong dbt Developer can improve materialization and execution strategy.
When metrics need to feed multiple:
a governed semantic layer can reduce duplicated metric definitions.
A dbt Developer with semantic modeling experience can help implement it.
A single dbt project may work well for a small team.
A larger data organization may need clearer:
This is where dbt Mesh-style architecture can become relevant.
An established organization may have years of transformation logic in:
A dbt Developer can help migrate that logic incrementally while preserving outputs and adding tests.
Strong dbt Developers combine advanced SQL with data modeling, software-engineering discipline, and deep knowledge of at least one data platform.
SQL is foundational.
Candidates should understand:
A candidate who knows dbt syntax but struggles with complex SQL will have difficulty owning a serious transformation layer.
Candidates should understand:
ref()Ask how they organize:
Strong candidates should be able to explain their project conventions and why they use them.
Candidates should understand:
and the tradeoffs between them.
Look for practical experience with:
Candidates should understand:
Testing strategy matters more than simply knowing the built-in test names.
Candidates should know how to:
If historical source changes matter to your product, assess direct snapshot experience.
Candidates should understand Jinja well enough to create reusable logic without making SQL unnecessarily abstract.
Candidates should know when macros make sense and when plain SQL remains clearer.
Look for knowledge of:
If governed metrics matter to your organization, assess experience with:
More mature teams may benefit from developers experienced with contracts and stable data interfaces.
For large organizations, relevant experience may include:
Candidates should be comfortable with:
A mature dbt workflow should validate changes before production.
Candidates may have experience with:
Relevant tools may include:
Snowflake experience matters when that's your warehouse.
Candidates should understand how dbt decisions affect Snowflake performance and spend.
BigQuery roles may require deeper understanding of:
Databricks-focused roles may need experience with:
Redshift environments require their own performance considerations.
Candidates should be able to identify whether a slow dbt job comes from:
Python can be useful for selected dbt environments and broader data workflows.
It's valuable without being mandatory for every dbt role.
Strong dbt Developers should create documentation that explains:
dbt Developers frequently work with:
They often need to resolve questions such as:
What exactly counts as an active customer?
That requires business understanding as well as SQL ability.
dbt Developer compensation depends on seniority, SQL depth, warehouse expertise, analytics engineering experience, and the maturity of the data environment.
South's current dbt Developer calculator lists an average U.S. salary of approximately $10,000 per month and an all-in monthly rate of approximately $7,200 for Latin American talent.
That's potential savings of around 30%.
The exact rate depends on the profile you need.
Junior developers can contribute to:
They work best when experienced analytics or data engineers can establish architecture and modeling standards.
Mid-level developers can usually own complete transformation workflows.
They may handle:
Senior developers can make broader technical decisions.
They may:
Many organizations use Analytics Engineer as the broader title for someone performing dbt-heavy work.
That profile may combine:
Larger or more mature environments may need deeper experience with:
Define those platform-specific requirements explicitly.
A strong dbt interview should test SQL, data modeling, testing, materializations, warehouse performance, project architecture, and business-metric judgment.
Avoid making the interview a quiz about dbt command syntax.
Ask:
Walk me through the largest dbt project you've owned.
Then ask:
Specific production examples reveal much more than certification terminology.
Ask:
How would you structure transformations from raw Stripe data to a finance-ready monthly revenue model?
Look for a sensible progression involving:
The exact structure can vary.
The reasoning matters more than following one folder convention mechanically.
Ask:
What does one row represent in this model?
A strong dbt Developer should ask this constantly.
Unclear grain creates:
Ask:
You have a five-billion-row event model that currently rebuilds every hour. What would you investigate?
Candidates may discuss:
Ask:
What tests would you add to an orders model?
Possible answers include:
Look for reasoning about what can actually break.
Ask:
A model contains complicated logic for classifying customers as new, retained, resurrected, or churned. How would you test the edge cases?
Unit tests may be particularly useful here.
Ask:
You see the same 25-line CASE statement copied across ten models. What would you do?
A macro might help.
The candidate should also consider whether the repeated logic belongs in an upstream model instead.
Ask:
When would you choose a view, table, incremental model, or ephemeral model?
Strong candidates should explain tradeoffs around:
Ask:
A foundational customer model changes. How do you determine which downstream datasets are affected?
Look for:
Ask:
Our dbt bill didn't increase, but Snowflake spending doubled after the project grew. How would you investigate it?
Strong candidates should understand that dbt's biggest cost may actually appear on the warehouse bill.
Ask:
Marketing and Finance use different definitions of new customer revenue. How would you resolve it?
The candidate should discuss:
rather than simply choosing one SQL query.
Ask:
A developer changes one staging model inside a 2,000-model project. What should happen before the pull request merges?
Look for intelligent CI rather than blindly rebuilding every model.
Ask:
Your company has 400 scheduled SQL scripts and stored procedures. How would you migrate them to dbt?
Strong candidates should propose a phased migration with:
rather than a big-bang rewrite.
Useful questions include:
Choose questions based on your environment.
A Snowflake/dbt Analytics Engineer and a Databricks-focused Data Engineer using dbt shouldn't have identical interviews.
These roles overlap heavily.
An Analytics Engineer is the broader role.
They commonly own:
dbt may be their primary development tool.
A dbt Developer is defined more specifically around deep expertise with the dbt framework.
Hire an Analytics Engineer when the responsibilities extend broadly across the analytics layer.
Use dbt Developer when hands-on dbt depth itself is a central requirement.
A Data Engineer typically owns a broader portion of the data platform.
They may handle:
A dbt Developer focuses more heavily on what happens after data reaches the analytical platform.
If source data isn't landing reliably in the warehouse yet, a Data Engineer may be the more immediate hire.
If ingestion works and the transformation layer has become the bottleneck, a dbt Developer can be the more specialized choice.
A SQL Developer specializes more broadly in SQL and database development.
They may work across:
A dbt Developer uses SQL inside a specific analytics engineering framework.
The role adds deeper requirements around:
A Data Warehouse Engineer focuses more heavily on the underlying warehouse architecture.
They may own:
A dbt Developer focuses more heavily on the transformation code running inside that platform.
The two roles often collaborate closely.
A BI Developer generally works farther downstream.
They may own:
A dbt Developer creates the trusted transformed datasets that BI systems consume.
South helps U.S. companies find dbt Developers across Latin America based on the warehouse, transformation architecture, modeling requirements, and seniority involved.
Start with your data stack.
Consider:
A developer maintaining 100 BigQuery models needs a different background from someone architecting a multi-team Snowflake/dbt environment with thousands of models.
South identifies dbt Developers across Latin America whose backgrounds align with your requirements.
Evaluation can include:
You receive a focused selection of candidates instead of sorting through a large number of general data applications.
Compare their:
Your data team interviews the candidates you want to meet.
Use real transformation examples, modeling exercises, incremental-model scenarios, testing questions, cost problems, and examples from your existing dbt project.
You make the final hiring decision and manage your dbt Developer 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.
dbt Developers collaborate frequently with:
Latin America's overlap with U.S. working hours supports:
Companies can access dbt Developers across Latin America with experience in SQL, Snowflake, BigQuery, Databricks, Redshift, Git, Airflow, dbt testing, Semantic Layer, data modeling, and modern analytics engineering practices.
A dbt Developer builds and maintains the transformation layer in a modern data platform.
Their work may include SQL models, tests, documentation, lineage, incremental models, macros, semantic metrics, CI/CD, and warehouse optimization.
South's current main calculator lists an all-in monthly rate of approximately $7,200 for Latin American dbt talent compared with an average U.S. salary of approximately $10,000 per month.
That's potential savings of approximately 30%.
Look for strong SQL, dbt models, data modeling, testing, incremental processing, Jinja/macros, Git, CI/CD, and production experience with your cloud data platform.
Yes.
SQL is the foundation of most dbt transformation work.
Advanced dbt knowledge can't compensate for weak SQL when the developer needs to build complex production models.
It depends on the role.
Python can be useful for selected transformations and wider data engineering work, while many dbt-focused Analytics Engineers spend most of their transformation time in SQL and Jinja.
Only if your company uses Snowflake.
BigQuery, Databricks, Redshift, and other dbt-supported platforms require their own optimization knowledge.
Analytics Engineer is the broader role.
dbt Developer emphasizes deep expertise with dbt specifically.
Many professionals can reasonably use either title.
A Data Engineer typically owns broader ingestion and data-platform infrastructure.
A dbt Developer focuses more deeply on transformation, modeling, testing, and analytics-ready data inside the warehouse.
Consider hiring one when your warehouse already receives data reliably but transformations are becoming inconsistent, expensive, poorly tested, difficult to document, or difficult for several teams to maintain.
Yes. Latin America has dbt Developers and Analytics Engineers with experience across SQL, Snowflake, BigQuery, Databricks, Redshift, Airflow, Git, data modeling, CI/CD, Semantic Layer, and modern analytics engineering, with working hours that can align closely with U.S. teams.
The right dbt Developer helps your team turn a warehouse full of raw tables into a tested, documented analytics layer where people can agree on what the numbers actually mean.
South helps U.S. companies find pre-vetted dbt Developers across Latin America based on dbt depth, SQL ability, warehouse expertise, modeling judgment, seniority, and communication.
Schedule a free call and find your next dbt Developer 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.
