Senior Data Engineer at Reap

Singapore, Hong Kong Island, Singapore -

Full Time

Start Date

Immediate

Expiry Date

02 Jun, 26

Salary

0.0

Posted On

04 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

ETL, ELT, APIs, Data Modeling, Data Governance, Data Security, Python, SQL, AWS, Snowflake, Big Data Processing, API Integrations, Troubleshooting, Debugging, Credit Cards, Payments

Industry

Information Technology & Services

Description

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: As part of a rapidly growing data team, we are looking to hire Senior Data Engineers to contribute to our dynamic and innovative projects. We are looking for someone who is ready for the challenge, is solutions-oriented, and thinks out of the box. The ideal candidate should be passionate about using data to drive impact across the organization and a collaborative mindset to work effectively within cross-functional teams. If you are enthusiastic about pushing boundaries, solving complex problems, and making a significant impact, we encourage you to apply and be part of our exciting journey in shaping the future of our products. Responsibilities: Design and development: Design, develop, and deploy scalable ETL/ELT pipelines and APIs for ingestion and transformation. Implement data modeling best practices for optimal accessibility, flexibility and query performance. Implement data governance practices, including data security, privacy, and compliance, to ensure data integrity and regulatory compliance. Collaboration: Work closely with cross-functional teams, including product managers, designers, and other engineers, to ensure seamless product development from concept to deployment. Influence product and cross-functional teams to identify data opportunities to drive impact. Continuous learning: Stay updated with the latest industry trends and technologies, ensuring our tech stack remains modern and competitive. Observability and Support: Build monitor and alerts for data pipelines monitoring, identify and resolve performance issues, troubleshoot data-related problems in collaboration with other teams, and ensure data platform SLAs are met. To be successful you will need to have: Experience: 6+ year work experience in data engineering and cloud platforms. Previous experience in a senior or lead engineering role. Technical proficiency: Expertise in ETL, data modeling, and cloud data warehousing. Strong programming skills in Python, SQL, AWS, Snowflake and related tech stack. Hands-on experience with big data processing and API integrations. Problem solving: Strong analytical and problem-solving skills, with a keen attention to detail and a passion for troubleshooting and debugging Experience in Credit Cards, Payments and AWS certification would be an advantage. Exposure to AI, machine learning, and predictive analytics is highly desirable. Benefits A Global & Dynamic Team Remote Work Friendly After submitting your application, please check your inbox for a confirmation email. If you don't see it, kindly check your spam or junk folder and adjust your settings to ensure future communication reaches your inbox. You can follow the steps here.

Responsibilities

Design, develop, and deploy scalable ETL/ELT pipelines and APIs for data ingestion and transformation, while implementing data modeling best practices and data governance policies. Collaborate with cross-functional teams to influence product development and ensure data opportunities drive organizational impact.