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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 to embed financial services that SMBs can then leverage, starting with revenue-based financing. We are a tight-knit team coming from organizations such as Google, Amazon, Nubank, Uber, Capital One, Mercado Libre, Globant and J.P. Morgan.
We are entering a new phase of growth following a strategic investment from Ant International, focused on rapidly expanding our partner footprint, strengthening our credit and underwriting capabilities, and scaling operations across multiple markets. As part of this growth journey, eligible team members have the opportunity to participate in R2 ’s Phantom Share Program, a performance-based incentive designed to align our team with the company’s long-term success and value creation. We believe in building a culture of ownership, where those who help create value share meaningfully in it.
As Data Engineering Tech Lead, you will be the senior technical authority for R2 's data platform and the day-to-day leader of the Data Engineering team. You will report to the Director of Data Management.
You will own the architecture, technical roadmap, tooling and vendor decisions, reliability and cost of the platform behind ingestion, data processing, underwriting and our enterprise data warehouse.
The role evolves in three stages:
Lead by influence. A hands-on lead engineer who sets technical direction and standards.
Run the team. Own planning, prioritization and delivery for the Data Engineering team day to day.
Manage people. Take on direct management of some of our Senior Data Engineers.
As we expand from Latin America into Europe, including a fully isolated EU data platform, you will shape how our data architecture scales across countries, legal entities and regulatory regimes.
What you will do
Architecture and technical direction Own the target architecture of the data platform, including ingestion, cleaning, processing, the warehouse, Airflow orchestration and the data flows behind underwriting.
Lead architecture decisions together with the Director of Data, and set the principles the team designs against.
Define and own the Data Engineering technical roadmap, balancing business priorities, platform health and tech debt.
Lead tool and vendor evaluation, selection and contract input.
Design a multi-country, multi-entity platform, including a fully isolated EU data platform and the data residency rules around it.
Stay hands-on in the most critical and complex parts of the stack: Snowflake, Airflow DAGs, dbt and Python.
Reliability, operations and cost Own the reliability, observability and SLAs of production pipelines.
Design the on-call rotation, incident response process and post-mortem practice.
Own Snowflake spend, including warehouse sizing, usage monitoring, budgets and cost-optimization practices.
Define and enforce engineering standards for modeling, testing, CI/CD, documentation and code review.
Introduce AI-enabled tooling and agents where they measurably improve productivity or data operations.
Team leadership and delivery Run the Data Engineering team day to day: planning, prioritization, delivery cadence and unblocking.
Project-manage data initiatives end to end: scope, estimates, plans, tracking, and clear communication of risks and trade-offs to stakeholders.
Mentor and grow Senior Data Engineers through design reviews, code reviews and regular feedback.
Take part in hiring, onboarding and shaping the team's structure as it grows.
Progressively take on people management for part of the team, including 1:1s, performance and career development.
Partner with Analytics, Product, Risk and Credit, ML, Engineering and Compliance to turn business needs into platform work.
Data governance, security and compliance Co-own data governance with the Director of Data, covering data quality, lineage, cataloging and access control.
Design how PII is handled across the platform, including classification, masking, encryption, Snowflake role-based access, retention and deletion.
Make sure the platform meets regulatory and audit requirements, including GDPR in Europe, LGPD in Brazil, central bank and local regulatory reporting, and investor and partner requirements such as Ant International's.
Translate regulatory requirements into technical controls, evidence and documentation that hold up in an audit.
Who you are
8–12+ years in Data Engineering or closely related roles.
Prior experience as a Principal or Staff Data Engineer, or as Tech Lead of a team of 4 or more engineers.
Proven team leadership and project management. You have run a team's delivery, led multi-quarter initiatives and managed stakeholders end to end.
Deep, hands-on production experience with Snowflake, including architecture, performance tuning, cost management and role-based security. This is required.
Advanced experience with Airflow and dbt in production.
Strong software engineering background with deep Python expertise, plus advanced SQL and dimensional / analytical data modeling.
Hands-on experience with ELT patterns and tools such as Airbyte or Fivetran.
Experience running data platforms on AWS.
Experience building or operating data platforms across multiple countries, legal entities or regulatory regimes. This is required.
Practical experience applying privacy and data protection regulations such as GDPR or LGPD, and building governance controls for PII.
Comfortable owning systems in production, including monitoring, alerting and incident response.
Able to explain technical trade-offs clearly to leadership and to Risk, Product and Compliance partners.
Professional English, plus Spanish or Portuguese.
Snowflake is a hard requirement. Profiles centered on Databricks or BigQuery without significant production Snowflake experience will not be a fit.
Bonus Experience in fintech, lending, credit risk or underwriting data.
Experience standing up a data platform in the EU, or an isolated regional platform with strict data residency.
Experience with data catalog, lineage or data contract tooling.
Experience integrating AI/ML tooling or agents into data workflows, or building feature pipelines for ML models.