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MoonPay
Staff Data Engineer
about 13 hours ago | 18 views | Be the first one to apply

Staff Data Engineer

Full-time
Remote

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

This is a hands-on Staff Engineer role focused on evolving and scaling our data platform as the company grows.

You’ll work on the systems that power analytics, product insights, integrations, and data science — helping ensure they are reliable, secure, and built to last in a regulated environment.

We’re looking for someone who enjoys solving complex systems problems end-to-end: from shaping architecture and making pragmatic design trade-offs, to contributing directly to code. Your impact will come from improving how our platform works today while helping teams make better long-term technical decisions.

This role is about influence and execution — leading through strong engineering judgment, collaboration, and hands-on contribution.

What You’ll Own Technical Direction: Contribute to the evolution of our data platform across streaming, batch, warehousing, governance, and data science enablement Help define and champion architectural principles that allow teams to build safely, consistently, and independently Work closely with compliance and security and a range of teams to design systems that meet reliability and regulatory requirements Identify long-term technical risks and guide pragmatic, incremental improvements Reliability, Scale & Performance: Improve the reliability, scalability, and cost-efficiency of our data platform as usage grows Evolve our streaming and warehouse infrastructure to support increasing real-time and batch workloads Strengthen observability, deployment automation, and cost governance across the data stack Reduce systemic complexity and eliminate fragile or redundant patterns Technical Leadership & Collaboration: Provide technical direction within the data platform domain through design reviews, mentoring, and hands-on contribution Influence adoption of shared services and common patterns through strong engineering practices and collaboration Partner with engineering managers and product managers to inform quarterly planning and longer-term technical priorities Support teams in making sound trade-offs between speed, quality, and long-term maintainability Engineering Quality & Craft: Mentor senior engineers and help grow technical depth across the organization Improve standards around data modeling, pipeline design, observability, and infrastructure as code Encourage thoughtful design documentation and clear decision-making Introduce pragmatic improvements to engineering workflows where they meaningfully increase productivity or quality About You Experience contributing to the architecture of a broad data platform Strong understanding of distributed and event-driven systems, along with modern data warehousing approaches A track record of aligning engineers and stakeholders around shared technical standards and great developer experience Experience designing for reliability, cost management, governance, and compliance at scale The ability to move between high-level architectural decisions and hands-on implementation Familiarity with working in regulated environments You’re a systems thinker who has helped build and scale data platforms used by multiple teams. You’re comfortable operating across architecture, hands-on delivery, and cross-team influence. Experience with tools such as Kafka or Pub/Sub, Airflow, Kubernetes, Terraform, Python, and SQL is helpful — but strong architectural judgment and systems thinking matter more than any specific technology. What you will be working with/on… Cloud-native infrastructure (we use GCP) Event streaming systems Real-time and batch processing Shared data tooling and reusable frameworks Observability and operational maturity Governance and data quality systems supporting compliance and auditability Infrastructure powering analytics, system integrations, data science, and ML workloads

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