Analytics Engineer

At R2, we believe that small and medium businesses are the productive engine of society. Small and medium businesses (SMBs) make up over 90% of companies in Latin America, yet they face a trillion-dollar credit gap. Our mission is to unlock SMBs’ potential by providing financial solutions that are tailored to their needs.

We are reimagining the financial infrastructure of Latin America - where SMBs financial needs are satisfied without ever having to go to a bank. R2 enables platforms in Latin America to embed financial services that SMBs can then leverage (starting with revenue-based financing and buy now pay later for business customers). We are a tight knit team coming from organizations such as Google, Amazon, Nubank, Uber, Capital One, Mercado Libre, Globant, J.P. Morgan, and Ualá.

We are backed by world-class investors such as Gradient Ventures (Google’s AI-focused fund), COMETA, General Catalyst, Hi Ventures, Y Combinator, Femsa Ventures, Prosus, Endeavor Catalyst, among others. We’re a data-intensive company. Data is the core of our product and the foundation of every decision we make.

Job Description

As an Analytics Engineer, you’ll sit at the intersection of software engineering and business analytics - helping build the data models, pipelines, and analyses that unlock insights across R2. You will be part of the Data Engineering team, and collaborate deeply with engineers, product teams, risk scientists, and business stakeholders to bring structure and clarity to our data.

Responsibilities

• Own and scale our data transformation layer, modeling raw data into clean, well-documented, and reliable data sets using dbt.
• Work closely with stakeholders to understand reporting and analytics needs, and translate them into scalable data models.
• Design data marts and semantic layers in our data warehouse to enable self-service analytics and KPI tracking.
• Partner with product, operations, and risk teams to enable decision-making through trustworthy data.
• Develop and maintain automated data quality checks and alerts to ensure accuracy and freshness.
• Help define and enforce best practices for data documentation, testing, and version control in the analytics stack.
• Contribute to the development and upkeep of our data platform, including Airflow / Dagster orchestration and ELT pipelines.
• Act as a bridge between data engineers and business users, ensuring that data is both technically sound and business-relevant.

Qualifications

Bonus Points

Location : São Paulo, Buenos Aires, Bogotá, Santiago de Chile, or CDMX

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