Remote Data Analyst Jobs for LatAm Professionals
Pay depends on the role, employer and experience. Review the published pay in current openings and confirm the terms before accepting an offer.
Data analyst is one of the fastest-growing remote roles at US companies. Every business that runs digital operations generates more data than it can interpret. LatAm professionals with SQL fluency, Python or R capability, and dashboard building skills in Tableau or Looker are consistently landing roles. The job is answering the questions that change decisions.
Quick answer
Remote remote data analyst roles call for relevant experience, professional English, and independent judgment. Check current openings for the responsibilities, location requirements, and any published pay.
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Owner: Puente editorial team
Hiring process: Puente recruiting team · Updated September 9, 2026
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Pay depends on the employer, role and experience. Review the salary and pay period in each current listing. Confirm working hours, benefits, payment fees and any commission terms before accepting an offer.
See current openings and published pay →What do US companies look for in a remote data analyst?
US companies look for strong SQL, practical Python, and careful statistical judgment. Your portfolio should include 3 to 5 complete projects with documented methods, while PostgreSQL, pandas, Tableau, Looker, or Power BI should appear in work you can explain clearly.
SQL fluency sufficient to write complex queries independently
SQL is non-negotiable. You need to write multi-table joins, window functions, CTEs, and subqueries without referencing documentation. Technical interviews at US companies include a live SQL problem. Expect questions on medians, moving averages, and cohort retention. If you freeze on a GROUP BY with a HAVING clause, you're not ready.
Python or R for data manipulation and analysis
Python with pandas and NumPy is the dominant tool. R is used in statistics-heavy environments like biotech and academic-adjacent research. You need to be able to clean a messy dataset, reshape it, run descriptive statistics, and create charts without being stuck on syntax. Jupyter notebooks for exploratory analysis is expected at most companies.
Dashboard building in Tableau, Looker, or Power BI
Stakeholders do not live in SQL query outputs. They need interactive dashboards that update on a schedule. Tableau, Looker, and Power BI are the three dominant platforms. Most US companies use one of these. If you have experience in only one, learn the basics of the others. The visual design of dashboards matters too, a well-structured dashboard tells a story.
Statistical thinking sufficient to avoid misleading conclusions
Correlation is not causation. Survivorship bias affects many business datasets. Small sample sizes produce unreliable conclusions. The best data analysts at US companies know when a finding is statistically real versus when it's noise, and they communicate this clearly to stakeholders who don't have a statistics background.
What does a remote data analyst do all day?
A remote data analyst turns a business question into SQL, validates the query, and presents the result in Looker or Tableau. One analysis may join 3 tables, use pandas for calculations, and end with a 200-word Notion summary for a stakeholder meeting.
Tuesday morning. The VP of Marketing sends you a Slack message: 'Can you pull the cohort retention for customers who came in through paid vs. organic channels? We're deciding on Q2 budget allocation.' You note the request and start scoping. What cohort period? What retention metric, 30-day, 90-day, or full LTV? You ask two clarifying questions and get answers by 10 AM.
You write the SQL query. It's a cohort analysis joining three tables: customers, acquisition_source, and subscription_events. The query takes 40 minutes to write and validate. You test it against a smaller date range first to confirm the logic, then run the full pull.
You bring the data into Python. You use pandas to calculate monthly retention rates by cohort and acquisition channel. You generate a heat map chart in matplotlib. The result is clear: organic customers have 72% 90-day retention vs. 54% for paid. The paid channel is acquiring customers who churn faster.
You build a Looker dashboard with the full analysis: retention curves by channel, CAC by channel, and LTV estimated at 12 months. You write a 200-word summary in Notion explaining the finding, the methodology, the confidence level (sample sizes are large enough to be reliable), and one recommendation: increase organic investment, investigate paid channel quality before Q2 budget increases.
At 3 PM you join the marketing review. You present the analysis. The VP asks three questions, you answer two immediately and flag one as requiring additional data you can pull tomorrow. The marketing team leaves the meeting with a specific decision to make. That's the job.
What skills do you need for a remote data analyst job?
You need SQL with joins, CTEs, and window functions, plus Python with pandas and a dashboard tool such as Tableau or Looker. A strong portfolio includes 3 to 5 projects and shows how you checked the data before recommending a decision.
Hard skills needed
- ✓SQL (complex queries: window functions, CTEs, joins)
- ✓Python (pandas, NumPy, matplotlib, seaborn)
- ✓R (tidyverse, ggplot2, for statistics-heavy roles)
- ✓Tableau or Looker (dashboard building)
- ✓Power BI (for Microsoft-environment companies)
- ✓Google BigQuery, Snowflake, or Redshift (data warehouses)
- ✓dbt (data build tool) for transformation pipelines
- ✓Excel and Google Sheets (advanced)
- ✓A/B testing and experiment design
Soft skills that close the hire
- ✓Statistical thinking and careful interpretation of data
- ✓Clear written communication for analysis summaries
- ✓Stakeholder question translation (what do they need?)
- ✓Skepticism of your own initial findings
- ✓English at B2 or above for async communication
- ✓Patience with messy, incomplete data
Where this role leads in 2-3 years
Data Analyst
You own the analysis function for one or two teams, build core dashboards, answer ad hoc questions, and establish a reputation for accurate, actionable insights.
Senior Data Analyst or Analytics Engineer
You work on more complex analytical problems, build data models in dbt, and guide business decisions at a strategic level.
Data Scientist or Head of Analytics
Strong analysts who develop machine learning skills move to data science. Those with leadership interest build analytics teams as Head of Data or Analytics Director.
From the blog
Questions about this role
Do I need a statistics or mathematics degree to be a data analyst?+
What database platforms do US companies use?+
Is Python or R more important?+
What makes a good data analyst portfolio?+
Will I work embedded with one team or across the whole company?+
How is this role different from a data scientist?+
How does Puente's 5-step vetting process work?
You complete these five steps in order so Puente and the hiring company can understand your experience, communication, and fit.
- Apply and record a video introduction. Submit your application with a short video introduction so we can assess how clearly you speak English and present yourself professionally.
- Recruiter interview. Complete a structured interview about your work history, working style, and ability to communicate clearly in written and spoken English.
- Client interview. Meet the US company so its team can decide whether your experience and working style fit the role and team.
- Background check. Complete a standard background verification before placement so you and the company can begin the working relationship with trust.
- AI tools certification. Complete Puente's free AI tools certification for your specific role before your first day with the company.
We aim to place great fits within 14 days. Timing depends on role availability and fit.
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