Compensación
Estimated- p25
- 64.272 US$
- mediana
- 75.614 US$
- p75
- 89.225 US$
Base anual estimada, USD · Actualizado junio de 2026. Cómo lo calculamos
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Data Analysts turn raw, messy data into answers a business can act on — writing SQL, building dashboards, validating metrics, and explaining what the numbers actually mean. This page covers what the role does, the skills that matter, and how demand and pay break down by region.
Mediana 75.614 US$
| Semana del | Nuevas vacantes |
|---|---|
| 13 abr | 1 |
| 18 may | 1 |
| 25 may | 3 |
| 1 jun | 8 |
| 8 jun | 1 |
| 15 jun | 28 |
| 22 jun | 8 |
Base anual estimada, USD · Actualizado junio de 2026. Cómo lo calculamos
Base anual media estimada en USD por región. Trabajar de forma remota amplía el rango en el que puedes ganar — y los mercados en los que puedes competir.
A Data Analyst sits between raw data and decisions. The core loop is: someone has a question, the analyst figures out what data could answer it, pulls and cleans that data (usually with SQL), analyzes it, and communicates the answer in a way a non-technical stakeholder can act on. That last step — turning a query result into a clear recommendation — is where a lot of the value lives. Building a dashboard is the visible output; defining the right metric and trusting it is the harder part.
The difference between a strong and weak analyst is rarely raw technical skill. Weak analysts answer the literal question asked, pull a number without checking whether the join inflated it, and present charts with no narrative. Strong analysts interrogate the question behind the question, sanity-check their results against known reality, flag when the data can't support a confident answer, and keep metric definitions consistent so two reports don't disagree. Intellectual honesty about uncertainty is a senior trait.
SQL is non-negotiable — comfortable joins, window functions, CTEs, and aggregation. On top of that: a BI tool (Tableau, Looker, or Power BI), spreadsheet fluency that's deeper than most expect, and a working grasp of statistics — distributions, significance, the traps in averages and A/B tests. Many analysts add Python or R for analysis the BI layer can't handle. Increasingly valuable is light data-modeling sense (understanding how the warehouse is structured, dbt awareness) and the communication skill to write a tight summary. The strongest analysts pair technical accuracy with genuine business curiosity.
Candidates on Diiirect complete a role-specific skills assessment instead of being judged on resume keywords alone. For Data Analysts that centers on practical SQL problems, metric-definition and data-interpretation scenarios, and questions that probe statistical judgment — spotting a misleading average, choosing the right cut of the data, recognizing when a result is too noisy to trust. Paired with portfolio and profile review, it gives hiring teams a reliable read on whether someone can actually do the work, not just describe it.
Realiza la evaluación de habilidades de Data Analyst para compararte y conectar con empresas que contratan directamente en diiirect. · unos 12 min
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Las cifras salariales son estimaciones a partir de vacantes públicas y la referencia BLS OEWS. Cómo lo calculamos.
What does a Data Analyst do?
They take business questions, pull and clean the relevant data (typically with SQL), analyze it, and communicate clear, actionable answers — often through dashboards and reports. The job is as much about asking the right question and explaining results as it is about the technical analysis.
What skills does a Data Analyst need?
Strong SQL is essential, alongside a BI/visualization tool (Tableau, Looker, or Power BI), solid spreadsheet skills, and a working grasp of statistics. Python or R and basic data-modeling awareness are common differentiators, and clear communication is what separates good from great.
How much does a Data Analyst earn?
Pay varies significantly by region, industry, and seniority, and overlaps with adjacent roles like analytics engineer. See the estimated compensation section above for regional medians.
What's the difference between a Data Analyst and a Data Scientist?
Analysts focus on describing and explaining what happened to drive decisions, leaning on SQL, BI tools, and applied statistics. Data scientists lean further into predictive modeling, machine learning, and heavier programming, though the line blurs at many companies.