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 a data warehouse engineer, you get the person who designs and builds the central warehouse your entire analytics operation runs on, the schemas, the pipelines that load it, and the performance and cost tuning that keep it fast and affordable as your data grows. South places full-time, pre-vetted data warehouse 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 warehouse foundation, not a slow, expensive pile of tables nobody designed.
A data warehouse engineer is a data professional who designs, builds, and optimizes the central data warehouse: defining the schemas and data models, building the pipelines that load data into it, and tuning it for performance, scale, and cost so the whole organization can analyze data reliably from one place.
The role exists because the warehouse is the foundation everything analytical sits on, and a badly designed foundation poisons everything above it. Reports, dashboards, machine learning, and every analyst's query all depend on the warehouse being well-modeled, reliably loaded, fast, and affordable. Get the design wrong, with the wrong schema, no clear grain, sloppy loading, no partitioning, and queries crawl, costs balloon, and analysts cannot trust what they pull. A data warehouse engineer owns that foundation deliberately. They are the specialist focused on the warehouse itself: how it is structured, how data gets into it, and how it performs at scale, which distinguishes them from a broader data engineer who handles the full pipeline ecosystem and from an analytics engineer who models data inside the warehouse for analysts.
The defining toolset centers on cloud data warehouses and data modeling. A strong data warehouse engineer is deep on at least one of Snowflake, BigQuery, or Redshift, and understands its specific performance and cost levers, clustering, partitioning, micro-partitions, slots, and warehouse sizing. They are expert at SQL and at data modeling, including dimensional modeling and approaches like star schemas, Kimball, and Data Vault, the discipline of structuring data so it is both correct and fast to query. They build ELT and ETL pipelines with tools like Fivetran, Airbyte, dbt, or custom code, orchestrate loads with Airflow or similar, and obsess over performance tuning and cost control, since cloud warehouses bill for compute and a poorly designed query can quietly cost a fortune. The role overlaps an ETL developer on the loading side and a Snowflake developer on platform depth.
What makes a data warehouse engineer great is the combination of design rigor and operational discipline. They model data so it is correct, scalable, and intuitive to query, and they keep the warehouse fast and cost-efficient as data volume grows from gigabytes to terabytes. They think in terms of the long-term health of the platform, not just getting today's data loaded. Companies in SaaS, fintech, and enterprise, anyone running real analytics at scale, rely on a strong warehouse foundation, which is why this specialist is worth far more than the cloud bill they help keep under control.
The clearest trigger is that your warehouse has gotten slow and expensive. When queries that used to take seconds now take minutes, when the cloud bill is climbing in a way nobody can explain, and when analysts complain that pulling data is painful, you have a warehouse design and performance problem. A data warehouse engineer redesigns the schema, tunes the queries, and gets your costs and performance back under control, and a single round of cost optimization on a large warehouse can pay their salary outright.
The second trigger is that your warehouse grew organically into a mess. If tables accumulated without a coherent model, if the same data lives in three places with no clear source of truth, and if nobody actually designed the schema, you have a foundation problem that gets worse as you build on it. A data warehouse engineer brings deliberate design, a clean model, clear grain, and reliable loading, that the rest of the analytics stack can trust.
The third trigger is scale. As data volume grows from gigabytes into terabytes, the naive approaches that worked early on break down, and you need someone who understands partitioning, clustering, and warehouse architecture to keep things fast and affordable. The earlier you bring that expertise in, the less debt you accumulate.
Who should not hire yet: an early-stage company with a small warehouse and simple data. If your data fits comfortably in a basic Snowflake or BigQuery setup, performance is fine, and costs are trivial, a dedicated warehouse engineer is premature. At that stage a strong data engineer or analytics engineer can handle the warehouse alongside their other work. The honest test is whether your warehouse has grown large, complex, slow, or expensive enough to need a dedicated owner. If queries are crawling, costs are climbing, or the schema is a mess, hire. If your data is still small and cheap, wait.
Evaluate data warehouse engineers on modeling depth and performance instincts first, because those are the load-bearing skills and the ones that separate a real warehouse engineer from someone who just loads tables. Give them a real scenario: here are our source systems and access patterns, how would you model this warehouse? A strong candidate asks about query patterns and grain, proposes a clean dimensional model, and explains how they would partition and cluster for performance. A weak one dumps source tables into the warehouse as-is and calls it done, which is exactly how warehouses become slow and expensive.
Test performance and cost knowledge directly, because in a cloud warehouse a careless design quietly burns money. They should explain how they diagnose a slow query, how they reduce warehouse cost without breaking workloads, and the specific levers of whichever platform they know best, which is where depth as a Snowflake developer shows. Probe SQL depth, since most performance and correctness issues live in the SQL, and probe data modeling judgment: dimensional design, slowly changing dimensions, and how they choose a grain.
Green flags: strong dimensional modeling, deep platform-specific performance and cost knowledge, expert SQL, and a clear focus on the long-term health of the warehouse. Someone who asks about query patterns before designing and talks about cost as a first-class concern is thinking like the role demands.
Red flags: someone who loads data without modeling it, who cannot explain how they tune a slow query or control cost, who has only used a warehouse as a passive store without designing one, or who treats the cloud bill as someone else's problem. Be wary of candidates whose experience is all small-scale, since the hard problems in this role only appear at volume.
Use these to test modeling, performance, and cost discipline:
A US-based data warehouse engineer typically costs around $11,000 per month in base salary, and more once you add equity, benefits, and recruiting fees. Strong warehouse engineers, the ones who model well and keep cloud costs under control, are in high demand at SaaS, fintech, and enterprise companies and command well above that. Through South, a comparably skilled data warehouse 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 deep modeling and performance skills the role requires are genuinely scarce. Through South, the same caliber of data warehouse 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 engineers trained on exactly this stack: Snowflake, BigQuery, Redshift, SQL, and modern ELT tooling. Many have designed and tuned warehouses for US and global SaaS and fintech companies and apply the same rigor their US peers do. They earn strong local wages that still produce major savings for a US employer. Because a good warehouse engineer makes your entire analytics operation faster and cheaper, and a single round of cost optimization can offset their salary, the return on the role is high and the lower cost makes it easy to justify.
Warehouse engineering is collaborative, operational work, and time zone overlap makes it function. The role lives on conversations with the analytics team about access patterns and models, on responding fast when a load fails or a query is slow before a reporting deadline, and on coordinating with data engineers and analysts who depend on the warehouse. A data warehouse engineer in Sao Paulo, Bogota, Mexico City, or Buenos Aires works your business hours, joins those conversations live, and fixes the broken load or the runaway cost the same day rather than across a time gap that turns every warehouse issue into a lost day of stalled analytics. For a foundational role the whole data team depends on, that overlap matters.
The talent depth is substantial and well matched to the role. Latin America has produced a strong generation of data engineers fluent in cloud warehouses and modeling, many with experience designing and operating warehouses for international companies. English proficiency is high among senior data professionals, which matters for a role built on partnering with US data and analytics teams.
Retention is a real advantage here, because warehouse knowledge compounds and is painful to lose. An engineer who knows your schema, your source systems, the reasoning behind every model, and the quirks of your cost profile is far more valuable in year two than a new hire relearning a complex warehouse from scratch. A full-time, dedicated engineer who is well compensated locally and embedded in your team tends to stay, so the foundation stays coherent and well-tuned rather than degrading every time someone leaves. 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 an analytics engineer.
South recruits, vets, and places full-time data warehouse engineers from across Latin America so you get a dedicated owner of your warehouse foundation, not a contractor who loads some tables and leaves you a slow, expensive mess. Every candidate is screened for what the role actually requires: deep experience with Snowflake, BigQuery, or Redshift, strong dimensional modeling, expert SQL, real ELT pipeline skills, and a genuine focus on performance and cost. We test with real modeling and tuning problems, because the combination of design rigor and operational discipline is exactly what separates a warehouse engineer who builds a fast, affordable foundation from one who builds an expensive bottleneck.
The process is fast. Most roles are filled in about two to four weeks, versus the two to three months a domestic search for these scarce skills typically takes. 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 data warehouse engineer works on your team, in your time zone, inside your warehouse, reporting to you. South handles sourcing and vetting and supports the placement, but the engineer is yours.
If your warehouse has gotten slow and expensive, or grew into a mess nobody designed, a data warehouse engineer is the hire that turns your foundation into a fast, affordable, trustworthy asset the whole company can build on, and hiring from Latin America makes it affordable. Book a call with South and we will place a vetted data warehouse engineer on your team in weeks.
A data warehouse 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 warehouse engineer makes your analytics faster and can offset their own salary through cloud cost optimization, the return easily justifies the cost.
Yes. South places data warehouse engineers from countries like Brazil, Colombia, Argentina, and Mexico whose business hours overlap with US time zones. This matters because the role lives on fast responses when a load fails or a query is slow before a deadline, and on live collaboration with the data and analytics teams that depend on the warehouse.
South screens for deep experience with Snowflake, BigQuery, or Redshift, strong dimensional modeling, expert SQL, and ELT pipeline skills with tools like Fivetran and dbt, plus a real focus on performance and cost optimization. Many also have orchestration experience with Airflow. We match for your specific platform and stack.
Most South placements happen in about two to four weeks, compared to the two to three months a domestic search commonly takes for these scarce modeling and performance skills. 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 handles the full pipeline ecosystem, moving and processing data across many systems. A data warehouse engineer specializes in the warehouse itself: designing its schemas and models, loading it reliably, and tuning it for performance and cost. The warehouse engineer is the specialist focused on making that central store fast, correct, and affordable.
Full-time and dedicated. South does not place gig or freelance workers. Your data warehouse engineer is a long-term member of your team, which matters because warehouse knowledge compounds and continuity keeps your foundation coherent, fast, and cost-efficient as your data grows.
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.
