Posted today · be early
Data Engineering Tech Lead (LatAm Only)
R2
WorldwideremotePosted 1 day ago
Skill Required
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Key highlights
- Required experience: 8–12+ years
- Key requirement: Production Snowflake experience (hard requirement)
- Key requirement: Multi-country/regulatory platform experience
- Key benefit: Phantom Share Program
Role overview
R2 is a fintech reimagining the financial infrastructure of Latin America to unlock the potential of small and medium businesses (SMBs) through embedded financial services, such as revenue-based financing. The company is entering a new growth phase following a strategic investment from Ant International, focusing on expanding its partner footprint, strengthening underwriting, and scaling operations across multiple markets, including Europe. 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, reporting to the Director of Data Management. You will own the architecture, technical roadmap, tooling, vendor decisions, reliability, and cost of the platform supporting ingestion, data processing, underwriting, and the enterprise data warehouse.
Responsibilities
- 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.
- 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.
- 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.
- 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.
Requirements
- 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.
Nice to have
- 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.
- Experience designing self-serve analytics platforms.
- Experience with cloud data cost management (FinOps) at scale.
Benefits
- 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.
- The chance to join a high-impact, mission-driven fintech.
- Ownership and impact in a fast-paced, growth-oriented startup.
- Cross-functional collaboration with exceptional teams.
- Exposure to complex regulatory challenges and innovation in embedded finance.
Additional details
- The role evolves in three stages: 1. Lead by influence (hands-on lead engineer); 2. Run the team (planning, prioritization, delivery); 3. Manage people (direct management of Senior Data Engineers).
- Locations: Buenos Aires, São Paulo, Bogotá, Santiago, Bogotá
- Originally posted on Himalayas
