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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When you hire an analytics engineer, you get the person who turns raw warehouse data into clean, tested, documented models the whole company can trust, the layer between your pipelines and your dashboards. South places full-time, pre-vetted analytics engineers from Latin America who work in your US time zone, cost roughly 53% less than a US hire, and start in about two to four weeks. You get a dedicated owner of your transformation layer, not a backlog of broken dashboards nobody believes.
An analytics engineer is a data professional who builds the transformation layer between raw data and analytics, modeling, testing, and documenting data in the warehouse so analysts and the business can trust it. They apply software engineering practices like version control, testing, and modularity to analytics work, most often using dbt on top of a cloud warehouse like Snowflake or BigQuery.
The role exists because of a gap that opened up in modern data teams. A data engineer builds the pipelines that load raw data into the warehouse. A data analyst queries the data and builds reports for the business. Between them sat a no-man's-land: who turns the messy raw tables into clean, well-defined, reusable models that everyone can build on? For years analysts hacked that work into ad hoc SQL and engineers treated it as not their job, and the result was the same broken state at company after company, conflicting numbers, duplicated logic, and dashboards no one trusted. The analytics engineer owns that middle layer deliberately, and the role has become one of the most valuable on a modern data team.
The defining toolset is dbt, the transformation framework that lets analytics engineers build modular SQL models with version control, automated testing, and generated documentation. They work on a cloud warehouse like Snowflake, BigQuery, or Redshift, write a great deal of SQL, manage their code in Git, and often orchestrate runs with a tool like Airflow or dbt Cloud. They build the semantic and metric layers that give the company one definition of revenue, one definition of an active user, one source of truth. Downstream, their models feed BI tools like Looker, Tableau, or Power BI, which is where the work overlaps with a BI developer. They live by the practices software engineers take for granted: code review, testing, modular design, and documentation, applied to data.
What makes an analytics engineer great is the blend of engineering discipline and analytical empathy. They write clean, tested, well-documented SQL that holds up as the business changes, but they also understand what the analysts and executives downstream actually need, so the models they build are useful rather than just correct. They are obsessive about data quality, because a single bad join silently corrupts every dashboard built on it, and they treat trust as the product. The best ones reduce the chaos: instead of fifty analysts each writing their own slightly different revenue query, there is one tested, documented revenue model everyone uses. Companies in SaaS, fintech, and enterprise rely on analytics engineers to make their data trustworthy and their analysts dramatically more productive.
The clearest trigger is that nobody trusts your numbers. When two dashboards show different revenue, when every analyst writes their own version of the same metric, and when leadership has stopped believing the reports, you have a transformation-layer problem. An analytics engineer builds one tested, documented set of models that everyone uses, and the day conflicting numbers stop reaching the executive team because there is finally a single source of truth, the hire has paid for itself.
The second trigger is that your analysts are drowning in modeling instead of analyzing. If your data analysts spend most of their time wrangling raw tables, rewriting the same joins, and untangling logic rather than answering business questions, you are wasting your most expensive analytical talent on plumbing. An analytics engineer builds the clean models once so analysts can move fast on top of them, which often makes a small data team feel twice as large.
The third trigger is scale and maturity. Once you have a real warehouse, multiple source systems, and a growing pile of business logic, that logic needs to live in tested, version-controlled models rather than scattered SQL and tribal knowledge. The longer you wait, the more technical debt accrues in your data, and analytics debt is brutal to pay down.
Who should not hire yet: an early-stage company with a small warehouse, one or two analysts, and simple data that a strong analyst can model directly. If your transformation needs are light and your numbers are not yet in dispute, a capable data analyst who knows dbt can cover both jobs for now. The honest test is whether your data has grown complex enough that the modeling layer needs a dedicated owner. If analysts are bottlenecked on plumbing or your numbers are no longer trusted, hire. If your data is still small and simple, an analytics engineer is premature.
Evaluate analytics engineers on SQL depth and engineering discipline first, because those are the load-bearing skills and the ones most often overstated. Give them a real modeling problem: here is a set of raw tables, build me a clean model for this business concept. A strong candidate writes clear, well-structured SQL, thinks about edge cases and grain, adds tests, and explains how they would document it. A weak one produces a query that technically works but is a tangle no one else could maintain. The difference between those two is the entire value of the role.
Test dbt fluency directly, because dbt is the spine of modern analytics engineering. They should talk concretely about model structure, staging and mart layers, testing, macros, and documentation, from real experience rather than a tutorial. Probe their sense of data quality: how they catch a silent bad join, how they test a model, what they do when a stakeholder reports a number that looks wrong. And probe the analytical empathy, because a model that is technically perfect but does not match what the business needs is a failure. Ask how they decide what to model and how they partner with analysts.
Green flags: clean and tested SQL, real dbt experience they can describe in detail, obsession with data quality, and the instinct to build for the analysts and stakeholders downstream. Someone who talks about a single source of truth, reusable models, and trust is thinking like the role demands.
Red flags: someone who writes SQL that works but no one else could maintain, who treats testing and documentation as optional, who has used dbt only superficially, or who builds models without understanding what the business will do with them. Be wary of candidates who cannot explain how they ensure data quality, since untested models silently poison everything built on them.
Use these to test SQL depth, dbt fluency, and analytical judgment:
A US-based analytics engineer typically costs around $11,000 per month in base salary, and more once you add equity, benefits, and recruiting fees. The role is in high demand, and strong analytics engineers at well-funded SaaS and fintech companies command well above that. Through South, a comparably skilled analytics engineer from Latin America runs closer to $5,150 per month, a savings of roughly 53%.
For a US hire, expect about $11,000 a month in base, plus equity and full benefits, with a search that often stretches two to three months because the role is competitive and genuinely scarce. Through South, the same caliber of analytics engineer from Latin America comes in around $5,150 a month, fully dedicated, working in your US time zone, with placement in roughly two to four weeks and no large upfront fee.
The gap reflects geography, not capability. Latin America has a deep and fast-growing pool of data professionals trained on the exact modern stack the role requires: dbt, Snowflake, BigQuery, Git, and the BI tools downstream. Many have built transformation layers for US and global SaaS and fintech companies and apply the same engineering discipline their US peers do. They earn strong local wages that still produce major savings for a US employer. Because a good analytics engineer multiplies the productivity of every analyst and restores trust in the company's numbers, the return on the role is high and the lower cost makes it easy to justify.
Analytics engineering is collaborative work, and time zone overlap makes that collaboration real. The role lives on conversations with analysts and stakeholders about what to model and why, on code review with the rest of the data team, and on quick turnarounds when a number looks off before a board meeting. An analytics engineer in Sao Paulo, Bogota, Mexico City, or Buenos Aires works your business hours, joins those conversations live, and fixes the broken model the same afternoon rather than across a time gap that turns every question into a day-long delay. For a role defined by trust and responsiveness, that overlap matters.
The talent depth is substantial and well matched to the role. Latin America has produced a strong generation of data engineers and analytics engineers fluent in the modern data stack, many with experience building dbt-based transformation layers for international companies. English proficiency is high among senior data professionals, which matters for a role built on partnering with US analysts and stakeholders to model the data they need.
Retention is a real advantage here, because data modeling knowledge compounds and is painful to lose. An analytics engineer who knows your source systems, the history behind every model, and the quirks of your business logic is far more valuable in year two than a new hire relearning it all. A full-time, dedicated engineer who is well compensated locally and embedded in your team tends to stay, so that knowledge accrues and your data foundation grows coherently rather than being re-litigated. South places engineers for long-term, full-time roles for exactly this reason, the same logic that makes Latin America strong for a data engineer or a data scientist.
South recruits, vets, and places full-time analytics engineers from across Latin America so you get a dedicated owner of your transformation layer, not a contractor who leaves you a pile of untested SQL. Every candidate is screened for what the role actually requires: expert SQL, real dbt experience, cloud warehouse depth in Snowflake or BigQuery, the engineering discipline of testing and version control, and the analytical empathy to build models the business actually uses. We test with real modeling problems, because the combination of clean engineering and analytical judgment is exactly what separates an analytics engineer who restores trust in your data from one who adds to the mess.
The process is fast. Most roles are filled in about two to four weeks, versus the two to three months a domestic analytics engineer search typically takes for a role this competitive. There are no large upfront fees and the pricing is straightforward, so you get an excellent engineer at a fraction of US cost rather than a recruiting markup. You own the relationship. Your analytics engineer works on your team, in your time zone, inside your warehouse and dbt project, reporting to you. South handles sourcing and vetting and supports the placement, but the engineer is yours.
If no one trusts your numbers, or your analysts are stuck doing plumbing instead of analysis, an analytics engineer is the hire that turns raw data into a trusted asset and makes your whole data team faster, and hiring from Latin America makes it affordable. Book a call with South and we will place a vetted analytics engineer on your team in weeks.
An analytics engineer through South typically runs around $5,150 per month for full-time, dedicated work, compared to roughly $11,000 per month for a comparable US hire, plus equity and benefits. That is about 53% in savings, with no large upfront recruiting fees. Because a strong analytics engineer multiplies every analyst's productivity and restores trust in your numbers, the return easily justifies the cost.
Yes. South places analytics engineers from countries like Brazil, Colombia, Argentina, and Mexico whose business hours overlap with US time zones. This matters because the role lives on live collaboration with analysts and stakeholders, code review with the data team, and fast turnarounds when a number looks off before a deadline.
South screens for expert SQL and hands-on dbt experience, plus cloud warehouse depth in Snowflake, BigQuery, or Redshift, version control with Git, and a strong data-quality and testing discipline. Many also have experience with semantic layers, orchestration tools like Airflow, and BI tools like Looker and Power BI. We match for your specific stack.
Most South placements happen in about two to four weeks, compared to the two to three months a domestic search commonly takes for this competitive role. South maintains a vetted pipeline of LatAm data talent, so you move straight to interviewing strong, pre-screened candidates instead of fighting the broader market.
A data engineer builds the pipelines that load raw data into the warehouse and manages the underlying infrastructure. An analytics engineer owns the transformation layer on top of that data, modeling, testing, and documenting it in tools like dbt so analysts and the business can trust it. The analytics engineer sits between the data engineer and the analyst.
Full-time and dedicated. South does not place gig or freelance workers. Your analytics engineer is a long-term member of your team, which matters because data modeling knowledge compounds and continuity keeps your transformation layer coherent and trustworthy as your business evolves.
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.
We recommend market pay which varies for each role. See our salary guide and success stories for some ideas.
Then, we have two different models:
Staffing (most popular) - We charge a small monthly fee for each employee's monthly salary to make the process hassle-free. The fee covers sourcing, recruiting, admin, payroll, compliance, ongoing support, and a free replacement if necessary at any point. There are no cancellation fees or minimum commitments. You only pay if you make a hire.
Headhunting - A one-time simple fee once we've found the perfect candidate. This comes with a 120-day replacement guarantee.
For both options, you only pay something if we find you someone great that you want to 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.
