Data Engineering Manager

About Reap

Reap is a global financial technology company headquartered in Hong Kong with employees across multiple countries. We enable financial connectivity and access for businesses worldwide by combining traditional finance with stablecoins for efficient money movement.

Through our stablecoin-powered corporate cards, payments, and expense management tools, we streamline financial operations and help businesses scale. Our APIs enable businesses to integrate stablecoin-enabled finance into their own products and services—from issuing Visa cards to facilitating cross-border payments.

Backed by leading investors including Index Ventures and HashKey Capital, Reap is building the future of borderless, stablecoin-enabled finance.

Role Overview

We are looking for a Hands-On Data Architecture and Engineering Manager to lead the design, build, and evolution of our data platforms while actively contributing to their implementation. This role combines technical leadership, architecture ownership, and people management, with a strong expectation of hands-on involvement.

You will define data architecture standards, lead delivery of critical data initiatives, and work directly in the codebase alongside your team. The ideal candidate is a practitioner at heart—someone who can design scalable systems, write production-quality code, and guide engineers through complex technical decisions.

Responsibilities

Hands-On Architecture & Engineering
• Design, build, and evolve end-to-end data architectures, covering ingestion, transformation, storage, analytics, and data access.
• Actively develop and review ETL/ELT pipelines, APIs, and data services, ensuring scalability, reliability, and performance.
• Implement and maintain data models optimized for analytics, operational use cases, and future extensibility.
• Lead the implementation of data governance controls, including security, privacy, access management, lineage, and compliance.
• Make architectural decisions with a bias toward practical, maintainable solutions over theoretical perfection.

Technical Leadership & Delivery
• Own delivery of data engineering initiatives from design through production, including trade-off decisions and technical debt management.
• Establish and enforce engineering standards, best practices, and review processes.
• Ensure data platforms meet availability, performance, and SLA expectations.
• Build and maintain observability for data pipelines, including monitoring, alerting, and incident response.

People Leadership
• Lead and mentor a team of data engineers through hands-on guidance, code reviews, and technical pairing.
• Set clear technical expectations and help engineers grow in system design, data modeling, and production ownership.
• Balance delivery responsibilities with coaching—this role writes code, but also raises the bar for the whole team.

Cross-Functional Collaboration
• Partner closely with product, analytics, engineering, and business stakeholders to translate requirements into scalable data solutions.
• Influence roadmap decisions by clearly articulating architectural implications, risks, and trade-offs.
• Identify high-impact data opportunities and drive them through execution.

Continuous Improvement
• Stay current with modern data architectures, cloud services, and emerging tooling.
• Continuously improve the reliability, performance, cost efficiency, and developer experience of the data platform.

To Be Successful, You Will Need

Experience
• 10+ years of experience in data engineering, with proven hands-on delivery in senior or lead roles.
• Experience leading engineers while remaining actively involved in implementation.

Technical Proficiency
• Strong expertise in ETL/ELT, data modeling, and data architecture.
• Advanced skills in Python, SQL, AWS, Snowflake, and related data tooling.
• Experience with big data processing, event-driven architectures, and API integrations.
• Practical knowledge of data observability, performance tuning, and production support.

Problem Solving & Leadership
• Strong analytical and troubleshooting skills, especially in complex, production data systems.
• Ability to make clear architectural decisions under real-world constraints.
• Comfortable switching between strategy, code, and people leadership—sometimes in the same day.

Nice to Have
• Experience in Credit Cards, Payments, or Financial Services.
• AWS certifications.
• Exposure to AI/ML platforms, feature stores, or predictive analytics enablement.

Benefits
• A Global & Dynamic Team
• Remote Work Friendly

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