Data Analyst

About the role

This role turns complex data into guidance for product and strategy across Cabify's problem spaces and country groups. The position helps improve city mobility by informing decisions with analytics.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Datasets from warehouses are processed with efficient SQL to support decision-making.

Results are shared through dashboards and reports built in Tableau or PowerBI, monitoring KPIs at state-of-the-art standards.

Data needs are defined with engineers, including requests for new app events, third-party integrations, and data model designs with attention to granularity and relationships.

Organizational data sources are built so business users can explore data in a trusted environment.

Requirements

Strong alignment with Cabify principles is taken seriously.

At least 2 years of professional experience as a Data Scientist or Data Analyst is required.

Complex SQL must be written and understood, including work with large datasets.

Tableau, Qlik, PowerBI, or similar data visualization tools must be used competently.

A graduate degree in Business Administration, Economics, Engineering, Statistics, or related fields is required.

Fluency in English and Spanish is required.

Nice to have

A Master's degree in Business Analytics, Business Intelligence, or related fields is a bonus.

Experience in Product Analytics with tools such as Amplitude, Datadog, Mixpanel, or similar is a bonus.

Skills & tools

Data warehouses such as Google BigQuery and Amazon Redshift are used for intensive analysis.

Practical notes

This role is based in Santiago de Chile and follows Cabify principles rigorously.

The position follows agile methods within analytics groups and emphasizes knowledge sharing.

Hybrid work includes two work-from-home days per week plus six weeks of remote work per year.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data analysis roles translate large datasets into clear insights for stakeholders.

Visualization tools such as Tableau enable self-service data access and sharing in analytics teams.

Data analysts collaborate closely with engineering and product teams to define metrics and data infrastructure.

Continuous learning is supported through internal programs and access to external courses.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.