Staff Engineer — Data Platform

Confirmed live in the last 24 hours

Yuno

London

Remote

Posted April 24, 2026

Job Description

Europe · Remote · Full Time · Staff-Level Individual Contributor · +8 Years of Experience

Who We Are

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.

About The Role

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.

If you are a Staff Engineer with passion and drive who enjoys solving complex data problems and driving engineering standards and initiatives end-to-end, then we are looking for you.

You will play a pivotal role within the Data team that powers Yuno and its payment platform, while helping co-design and implement an architecture that enables the entire organization to operate on reliable, fast, and trustworthy data.

Your Contribution Will Be

The stack is modern: StarRocks as our primary analytical layer, Flink for processing, DBT for transformation, Airflow for orchestration and various tooling for surfacing insights.

The hard work of making it super reliable is still in front of us — and that's exactly why this role exists.

Technical Leadership

Define architecture within the data platform, structure and deliver projects and initiatives end-to-end.
Act as a technical reference point for the Data team, setting quality standards, testing, observability, data modeling, and documentation.
Lead the design and implementation of scalable, low-latency data pipelines that process high-volume payment transactions in real time.
Champion an AI-first engineering culture, establishing standards for AI-assisted development, automated data quality testing, and LLM-powered data workflows.

Hands-On Engineering

Design and build data pipelines for large volumes of payment data that are performant, reliable, and correct — not just fast.
Design scalable data models that support business-critical use cases: fraud detection, revenue analytics, payment success rate optimization, regulatory reporting.
Own platform reliability — SLAs, data quality, alerting, and incident response for data services.
Ensure secure data handling practices aligned with PCI-DSS, GDPR, and other compliance frameworks relevant to the payments industry.

Cross-Functional Impact

Partner with Product, AI, and Finance teams to translate business needs into scalable data solutions.
Contribute to the roadmap of the data platform and proactively identify opportunities to unlock new business value through data.
Mentor senior and mid-level engineers, raising the technical bar across the team through code reviews, design reviews, and knowledge-sharing sessions.
Collaborate with Data Consumers (analysts, data scientists, product managers) to ensure data products are reliable, well-documented, and fit for purpose.

Skills You Need

Minimum Qualifications

8+ years of experience in data engineering, software engineering, or a related field, with at least 2 years operating at a staff or principal level.
Deep expertise in designing and building large-scale data platforms — streaming, batch, or hybrid architectures.
Hands-on experience with Spark, Flink, Kafka, StarRocks, or equivalent.
Strong Python and SQL skills; comfort working across multiple languages and paradigms.
Solid understanding of data modeling techniques: dimensional modeling, Data Vault, or lakehouse patterns.
Experience with cloud data infrastructure (AWS, GCP, or Azure), including managed services for storage, compute, and orchestration.
Strong grasp of data quality, observability, and governance principles.
Proven ability to set standards and lead technical initiatives across multiple teams without direct authority.
Professional proficiency in English — written and spoken.