About the company
MoonPay is the world’s leading web3 infrastructure company. We provide end-to-end solutions for payments, enterprise-scale smart contract development, and digital asset management. Many of the world’s most iconic brands rely on MoonPay to power their web3 strategies and ideas.
Job Summary
Day-to-day, you will:
📍Architect, design, and develop our data platform capabilities. This means getting hands-on to build the robust foundations our data and AI capabilities rely on. Including real-time streaming, orchestration, data warehousing and governance. We value deep expertise in at least two of these core areas. 📍 Actively contribute to our technical vision and roadmap, including the estimation, planning, and execution of complex data platform projects, including new service integrations and major infrastructure upgrades 📍 Contribute and improve our existing data platform codebase, data pipelines, and Infrastructure as Code (IaC), focusing on scalability, reliability, performance, and cost-effectiveness 📍 Collaborate across engineering and analytics teams to deliver reliable platform capabilities that directly empower them, and to ensure these services are well-adopted and impactful 📍 Work closely with engineers, analysts, and data scientists to understand their needs, define clear requirements, and develop effective implementation plans for platform enhancements 📍 Write well-tested, high-quality, and performant code for data processing, platform automation, and infrastructure management
As a Staff Data Engineer, you will also:
📍Lead technical initiatives for the data platform from an engineering perspective, partnering effectively with stakeholders to drive these from concept through to successful delivery and adoption 📍 Mentor and coach engineers and other technical team members, helping to elevate their skills and fostering best practices in data engineering and system design 📍 Champion high standards for code quality, testing, and documentation within the data platform, and proactively identify and strategize on managing technical debt. 📍 Define and contribute to our overall data engineering standards, patterns, and best practices, including areas like data modeling, pipeline design, IaC, monitoring, and alerting for our data systems
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