Cómo contratar un/a Data Analyst
Data analysts turn raw data into the insights that drive daily business decisions. They are the bridge between data infrastructure and business teams — translating questions into queries, and query results into clear recommendations. Hiring the right analyst means finding someone who is both technically sharp and deeply curious about the business.
Qué buscar
- Strong SQL — this is the core skill, not optional
- Business acumen: ability to understand what a metric actually means for the company
- Experience with BI tools (Tableau, Looker, Power BI, or Metabase)
- Clear written and verbal communication — findings must be understood by people who don't look at data all day
- Proactive attitude: identifies problems before being asked, rather than just answering requests
- Attention to data quality: flags anomalies rather than accepting numbers at face value
El proceso de contratación
- 1
SQL skill assessment
Give a multi-table SQL problem with aggregations, window functions, and filtering. This is the clearest single signal of readiness.
- 2
Business case exercise
Give a business scenario (e.g., 'revenue dropped 12% last week — how do you investigate?') and evaluate their structured diagnostic approach.
- 3
Dashboard or report review
Show a real or sample dashboard and ask: 'What would you add, remove, or change, and why?' Tests product taste and communication clarity.
- 4
Stakeholder communication interview
Ask how they handle conflicting data requests, urgent deadlines, and business partners who misinterpret data.
Consejos para la entrevista
- Give a broken or misleading chart and ask them to spot the issue — tests data literacy and critical thinking
- Ask 'Describe a metric you created that stakeholders actually used' — looks for real impact
- Probe on self-service: 'How do you build dashboards that your business partners can trust and use independently?'
- Ask how they prioritize a backlog of ad-hoc requests
Señales de alerta
- Weak SQL — can't write a self-join or a window function from scratch
- Answers questions with data without questioning whether the question itself is the right one
- No experience presenting findings to leadership or business stakeholders
- Treats every number at face value — no instinct for data quality issues
- Reactive only — never proactively identified an insight the business didn't ask for
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