Senior Data Engineer

<h2><strong>About Thunes</strong></h2>
<p>Thunes is the Smart Superhighway for money movement around the world. Thunes’ proprietary Direct Global Network allows Members to make payments in real-time in over 130 countries and more than 80 currencies. Thunes’ Network connects directly to over 7 billion mobile wallets and bank accounts worldwide, via more than 350 different payment methods, such as GCash, M-Pesa, Airtel, MTN, Orange, JazzCash, Easypaisa, AliPay, WeChat Pay and many more.</p>
<p>Members of Thunes’ Direct Global Network include gig economy giants like Uber and Deliveroo, super-apps like Grab and WeChat, MTOs, fintechs, PSPs and banks. Thunes’ Direct Global Network differentiates itself through its worldwide reach, in-house Smart Treasury Management Platform and Fortress Compliance Infrastructure, ensuring Members of the Network receive unrivalled speed, control, visibility, protection and cost efficiencies when making real-time payments globally. Headquartered in Singapore, Thunes has offices in 12 locations, including Barcelona, Beijing, Dubai, London, Manila, Nairobi, Paris, Riyadh, San Francisco, Sao Paulo and Shanghai. For more information, visit: https://www.thunes.com</p>
<h2><strong>Role Overview</strong></h2>
<p>We are seeking a highly skilled Data Engineer to drive the development and optimization of our data platform. This position is designed for a technical expert who excels at the intersection of data engineering, infrastructure automation, and business logic. The successful candidate will be responsible for building robust data lifecycles, ensuring the scalability of our cloud-native environment, and maintaining the integrity of our streaming and batch processing systems.</p>
<h2><strong>1. Job Responsibilities</strong></h2>
<ul>
<li><strong>Data Platform Engineering:</strong> Design, implement, and scale end-to-end data pipelines using Python and SQL-based transformations within a cloud-native data lake environment.</li>
<li><strong>Infrastructure Orchestration:</strong> Manage and scale containerized data workloads and automated workflow orchestration (using tools like Apache Airflow) and Infrastructure as Code (using Terraform) or similar functional equivalents.</li>
<li><strong>Streaming &amp; Real-time Integration:</strong> Maintain and evolve real-time data replication services and stream processing architectures (utilizing Apache Kafka or similar distributed messaging systems) to ensure low-latency data availability.</li>
<li><strong>Data Quality &amp; Governance:</strong> Establish and implement Data Governance frameworks and automated validation protocols to ensure the highest standards of data accuracy, compliance, and platform reliability.</li>
<li><strong>Business Logic &amp; Data Modeling:</strong> Partner with cross-functional stakeholders to interpret complex business requirements and translate them into performant data models for reporting and business intelligence.</li>
<li><strong>Systems Architecture:</strong> Provide technical insights into the continuous improvement of the data platform, ensuring the stack remains resilient and performant as data volumes and complexity increase.</li>
</ul>
<h2><strong>2. Candidate Requirements</strong></h2>
<ul>
<li><strong>Professional Experience:</strong> A proven track record in a Data Engineering capacity, with extensive experience managing and scaling modern, distributed data platforms.</li>
<li><strong>Cloud Platform Expertise:</strong> Deep experience managing cloud-native data ecosystems, with a strong preference for candidates familiar with AWS or GCP environments.</li>
<li><strong>Workflow &amp; Infrastructure Tools:</strong> Proficiency in workflow orchestration and Infrastructure as Code (IaC); specific experience with Apache Airflow and Terraform is highly preferred, though experience with similar tools (e.g., Prefect, Dagster, Pulumi) will be considered.</li>
<li><strong>Streaming Systems Depth:</strong> Demonstrated experience in configuring, maintaining, and troubleshooting distributed messaging and real-time data replication systems such as Apache Kafka or equivalent technologies (e.g., Amazon Kinesis, Google Pub/Sub).</li>
<li><strong>Data Governance &amp; Quality:</strong> Solid understanding of Data Governance principles, including data lineage, metadata management, and automated quality checks.</li>
<li><strong>Advanced Engineering Skills:</strong> Exceptional Python programming capabilities and advanced SQL proficiency, with a focus on writing clean, modular, and highly testable code.</li>
<li><strong>Business Sense:</strong> Strong analytical aptitude with the ability to interface directly with business units, understand underlying data logic, and architect schemas that support complex organizational reporting and data product needs.</li>
</ul>
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