Engineering Manager – Data Platform

At Yuno, we are building the payment infrastructure that allows all companies to participate in the global market. Founded by seasoned experts from the payments and tech industries — including the team behind Rappi, one of Latin America's most ambitious tech companies — our technology provides access to leading payment capabilities, enabling companies to engage customers confidently and maintain global operations through seamless integrations.

We empower high-performing teams at brands like InDrive, McDonald's, Rappi, and Viva Aerobus to connect to 300+ payment methods worldwide via a single API. By leveraging advanced AI and the latest technologies, we orchestrate smart routing and fraud prevention across 80+ countries.

We are orchestrating a high-performing data team that works with pace and enthusiasm!

Yuno moves money across borders for companies that can't afford for payments to fail. Our data platform is what makes that visible — to our product teams, our clients, and ourselves.

As an Engineering Manager within the Data team, you will lead a team of data engineers responsible for the platform that processes billions of payment events across 80+ countries. 

You will own both the people strategy and set technical direction for your team that sits at the core of Yuno's business: enabling fraud detection, revenue analytics, payment optimization, and data-driven product decisions. You will operate in a fast-moving, global environment where data is mission-critical.

• Lead and develop a multidisciplinary data engineering team, fostering a culture of technical excellence, ownership, and continuous improvement.
• Mentor engineers at all levels — supporting their growth through coaching, structured feedback, and clear career expectations.
• Drive hiring processes to attract and retain top data engineering talent globally.
• Create an environment where engineers are empowered to take ownership and deliver with autonomy and pace.

• Own the full lifecycle for your team — from ingestion and transformation to storage, serving, and observability.
• Drive hands-on technical contribution through architecture design, code reviews, and complex troubleshooting, setting the technical bar for your team.
• Set and enforce best practices across data modeling, pipeline reliability, testing, data quality, and documentation.
• Guide architectural decisions for high-throughput, real-time and batch data systems, ensuring they are scalable, maintainable, and cost-efficient.
• Ensure the team follows secure data handling practices aligned with PCI-DSS, GDPR, and other compliance frameworks applicable to the payments industry.
• Champion an AI-first engineering culture, setting standards for AI-assisted development, automated data quality testing, and LLM-powered workflows — ensuring your team treats these tools as a default, not an afterthought.

• Collaborate closely with Product, Analytics, Machine Learning, Finance, and Compliance teams in an agile environment to deliver against a fast-moving roadmap.
• Bridge the gap between data consumers (analysts, data scientists, product managers) and the engineering team, ensuring data products are reliable, well-documented, and trusted across the organization.
• Drive the evolution of data infrastructure to support new markets, new payment providers, and growing regulatory requirements.
• Translate business priorities into engineering goals, managing trade-offs between speed, reliability, and technical debt.

• Experience managing and growing data or software engineering teams, including hiring, coaching, and performance management.
• Strong ability to drive technical decision-making and manage competing priorities in a fast-paced environment.
• Excellent communication skills — able to engage effectively with both technical and non-technical stakeholders.
• Solid hands-on data or software engineering background: experience designing data pipelines, data models, and platform architecture at scale.
• Proficiency in Python and/or SQL; comfort navigating across modern data stacks.
• Deep understanding of streaming and batch processing architectures – Kafka, Spark, Flink, Airflow, or equivalent.
• Experience with cloud data infrastructure (AWS, GCP, or Azure) and modern data platform tools (e.g., dbt, data lakehouse patterns).
• Knowledge of data quality, observability, and governance principles.
• Champion of AI-first development — experience setting standards for AI-assisted workflows, automated testing, and code generation using LLMs and tools like Claude Code or similar.
• Experience delivering in agile environments, adapting processes to what actually works for the team.
• Professional proficiency in English — written and spoken.

• Experience in the payments or fintech industry.
• Familiarity with real-time analytics, event-driven architectures, and high-volume transactional data.
• Exposure to ML platform design or feature store infrastructure.
• Experience with DevOps practices applied to data: CI/CD for pipelines, infrastructure as code, and data contracts.