Get Data Analysts
Business and product data analysts from Brazil, Argentina, and Colombia who turn raw data into decisions. They write complex SQL, build dashboards people actually use, and know how to ask the right question before they query anything.
Senior data analysts in the US earn $110k-$150k. LATAM equivalents deliver at $45k-$75k — a compelling ratio for roles that create significant ongoing business value.
Data analysts work closely with product, marketing, and ops teams. LATAM's 4-8 hour overlap with US business hours makes weekly reviews, ad hoc queries, and dashboard feedback cycles practical.
Data analysis involves communicating findings to stakeholders who make decisions. LATAM analysts in our network are comfortable presenting in English and writing clear analysis summaries.
Statistics, economics, and engineering graduates from LATAM universities often move into data analysis roles. The quantitative foundation matters for getting analysis right.
LATAM data analysts increasingly work in dbt, Snowflake, and Metabase — the modern data stack that replaced legacy BI tools. They know the analytics engineering layer, not just the reporting layer.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 75-minute live session covering three areas. First, SQL: a multi-step query challenge involving window functions, CTEs, aggregations, and a tricky edge case. We look for correct results and clean, readable SQL — not just functional queries. Second, business analysis: given a dataset and a business question (e.g., 'retention is declining — why?'), candidates walk through their analysis approach: what to look at first, what hypotheses to test, and how to present findings. Third, dashboard design: given a list of KPIs, they describe how they'd structure a dashboard — which metrics are primary, which are secondary, and what they'd avoid cluttering.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
3+ years experience
Buenos Aires, Argentina
6+ years experience
Medellín, Colombia
8+ years experience
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Request matched profilesWhat to expect
Data analysts in our network ask the business question before they write the query. They don't deliver dashboards that nobody uses — they work with stakeholders to understand what decisions the data should support. They use AI tools to move faster on the routine parts of analysis without letting automation replace thinking.
AI-assisted SQL generation with Copilot or ChatGPT for initial query drafts on unfamiliar schema — they review and test the output rather than treating it as final.
AI-powered narrative generation for recurring reports: they use tools like Notably or AI-assisted templates to draft the written summary and focus their own time on interpretation.
They build dbt models with tests and documentation so the data team doesn't have to re-explain the same metric definition every month.
Dashboard design is intentional: fewer metrics, clearer hierarchy, and drill-down capability only where it adds value. They resist stakeholder requests that add noise.
They communicate data limitations proactively: if a dataset has a known sampling bias or a metric has a definitional edge case, it goes in the report — not in a footnote nobody reads.
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Python (Pandas, NumPy, visualization)