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 Data Analyst collects, cleans, queries, analyzes, and communicates data to help a company understand what is happening across the business.
They sit between the underlying data infrastructure and the people making decisions.
Typical Data Analyst responsibilities include:
The role varies by company.
A Data Analyst at a SaaS startup might spend most of their time analyzing product usage, activation, and retention.
An analyst at an e-commerce company may focus more on customers, products, marketing channels, conversion, and repeat purchases.
A finance-focused analyst may work heavily with forecasts, revenue, margins, and operational performance.
You should consider hiring a dedicated Data Analyst when data exists across the business but your team struggles to turn it into consistent answers.
If finance, marketing, sales, and product all report different versions of the same metric, you need stronger analytical ownership.
A Data Analyst can help establish reliable definitions, validate data, and create consistent reporting.
Questions such as these shouldn't require days of manual work:
A dedicated analyst creates the capacity to investigate those questions consistently.
Spreadsheets are useful, but dozens of manually maintained reports can create duplicated work and inconsistent calculations.
A Data Analyst can centralize recurring analysis and move appropriate reporting into dashboards.
Data Engineers should primarily focus on pipelines, infrastructure, warehouses, and reliable data models.
If they're constantly pulled into ad hoc reporting requests, a Data Analyst can take ownership of the business-analysis layer.
As products grow, intuition becomes less reliable.
A Data Analyst can help Product Managers and designers understand:
As budgets increase, companies need to understand which campaigns, channels, audiences, and customer segments actually generate value.
A Data Analyst can provide greater depth than platform-level marketing reports alone.
If someone spends several days every month copying figures into spreadsheets or presentations, there may be an opportunity to automate the process.
Companies become more data-driven when people can get trustworthy answers without waiting weeks for them.
A Data Analyst creates a dedicated bridge between business questions and available data.
The ideal background depends on the type of analysis your company needs.
For most roles, I would prioritize practical analytical ability over a specific degree.
SQL should be one of the first areas you evaluate.
A Data Analyst should be comfortable with:
More advanced roles may also require query optimization and deeper understanding of warehouse structures.
Candidates should know how to communicate information visually.
Experience with tools such as Tableau, Power BI, Looker, or similar platforms is useful when dashboards are part of the job.
Look beyond whether they know where the buttons are.
Evaluate whether they understand which visualization best communicates a particular question.
A Data Analyst should understand core statistical concepts well enough to avoid misleading conclusions.
Depending on the role, this can include:
Give candidates business questions rather than only technical exercises.
Strong analysts should be able to determine which data they need, what assumptions they're making, and what the result means for the company.
Candidates need to recognize that data can be incomplete or incorrect.
Ask how they validate a dataset before presenting results.
Spreadsheets remain valuable for ad hoc analysis, executive reporting, financial modeling, and collaboration.
Strong spreadsheet skills can complement SQL rather than replace it.
Python or R can be useful when the role requires more complex data manipulation, automation, statistics, or exploratory analysis.
They're valuable qualifications, but they shouldn't automatically outweigh strong SQL and business thinking for a traditional Data Analyst role.
Analysts need to explain their findings to people who may know little about SQL or statistics.
Look for candidates who can turn a complex analysis into a concise explanation of:
What happened, why it matters, and what the business should look at next.
Industry or functional experience can shorten the learning curve.
Depending on your company, you may prefer someone with experience in:
Data Analyst compensation varies by experience, technical depth, specialization, and market.
South's current Data Analyst calculator lists an average U.S. salary of approximately $7,500 per month and an all-in monthly rate of approximately $3,875 for Latin American talent, representing potential savings of up to 48%.
Your actual rate will depend on the role.
Junior analysts can support:
They work best when the organization already has established metrics, data infrastructure, and analytical leadership.
Mid-level analysts can typically take greater ownership of business questions.
They may independently:
Senior analysts handle more ambiguous questions and often influence how the organization thinks about metrics.
They may:
Some roles require additional expertise.
Examples include:
Specialized business knowledge can be just as important as additional technical tools.
A strong Data Analyst interview should test SQL, analytical reasoning, business judgment, visualization, and communication.
Avoid turning the entire process into abstract technical trivia.
Ask:
Tell me about an analysis you completed that changed a business decision.
Then ask:
This reveals whether the candidate has actually used analytics to influence decisions.
Give candidates a realistic dataset and question.
For example:
Using customers, subscriptions, and transactions tables, calculate monthly revenue by customer cohort.
Evaluate:
Getting the final number matters, but how the candidate approaches the problem also provides valuable signal.
Ask:
Conversion dropped 20% last week. How would you investigate it?
A strong candidate should want to segment the problem.
They may investigate:
The best candidates usually ask questions before jumping to an explanation.
Show the candidate a dashboard and ask:
What would you improve?
Or give them a dataset and ask which chart they would use to communicate the main finding.
You want someone who can simplify information rather than decorate it.
Use statistical questions that resemble the work.
For example:
We ran an A/B test and variant B converted 5% better. What would you need to know before recommending we launch it?
Look for awareness of sample size, statistical significance, experiment design, duration, segmentation, and business impact.
Give the candidate a complex finding and ask them to explain it as if they were talking to the CEO.
The analyst should be able to remove technical detail without removing meaning.
Useful questions include:
Choose questions based on the work they'll perform rather than asking every candidate the same generic technical list.
A Data Analyst primarily uses data to answer business questions.
A Data Engineer primarily builds and maintains the infrastructure that makes that analysis possible.
A Data Engineer may own:
A Data Analyst may own:
If your biggest problem is getting reliable data into the warehouse, hire a Data Engineer.
If the data already exists but your organization struggles to turn it into decisions, hire a Data Analyst.
Hire a Data Analyst when your main questions involve:
A Data Scientist is generally a stronger fit when the work requires:
Many growing companies need a strong analyst before they need a dedicated Data Scientist.
South helps U.S. companies identify Data Analysts across Latin America who match their data stack, industry, analytical needs, and level of seniority.
Start with the questions they'll be expected to answer.
Consider:
A Product Data Analyst and a Financial Data Analyst may require substantially different backgrounds.
South searches for Latin American Data Analysts aligned with your requirements.
Evaluation can include areas such as:
You receive a focused selection of candidates whose backgrounds align with the role.
Compare:
Your team meets the candidates you want to consider.
Use SQL exercises, analytical scenarios, portfolio or dashboard walkthroughs, and business questions to evaluate how they would perform in your environment.
You make the final hiring decision and manage your analyst 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 provides a free replacement if you need to make a change.
Analytics may be remote-friendly, but the work still benefits substantially from real-time communication.
Analysts need to ask stakeholders questions, clarify metric definitions, discuss unusual results, work with engineers, and present findings.
Latin America's overlap with U.S. working hours can support:
Companies can access Data Analysts across major Latin American talent markets while keeping their analytical function closely connected to U.S. teams.
A Data Analyst queries, cleans, analyzes, visualizes, and communicates data to answer business questions. They commonly work with SQL, dashboards, spreadsheets, product data, and business metrics.
Compensation varies by seniority and specialization. South's current calculator shows an all-in monthly rate of approximately $3,875 for Latin American talent compared with an average U.S. salary of around $7,500 per month.
Strong SQL, analytical reasoning, data visualization, business judgment, data cleaning, statistics, and communication are among the most important qualifications.
Python or R may also be valuable for more technically demanding positions.
Not always.
For many business-analysis roles, strong SQL, spreadsheets, and BI skills are more important.
Python becomes more useful for automation, complex transformations, statistical analysis, and larger exploratory projects.
Common tools include SQL, Excel, Google Sheets, Tableau, Power BI, Looker, Snowflake, BigQuery, Redshift, Python, R, dbt, and Jupyter.
You don't need every candidate to know every tool. Match the requirements to your existing stack.
A Data Analyst generally spends more time querying and interpreting quantitative data.
A Business Analyst often focuses more broadly on business requirements, processes, systems, and stakeholder needs.
The roles can overlap depending on the company.
A common signal is when your company has enough data to influence decisions but founders, Product Managers, marketers, or engineers are spending substantial time manually producing analyses and reports.
Yes. Companies can hire Latin American Data Analysts with experience in SaaS, product analytics, marketing, finance, e-commerce, operations, and other specialties while maintaining substantial overlap with U.S. business hours.
The right Data Analyst helps your company move from having data to actually understanding what it means.
South helps U.S. companies find pre-vetted Data Analysts across Latin America based on SQL ability, analytical thinking, visualization skills, industry experience, seniority, and communication.
Schedule a call and find your next Data Analyst 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.
