Staff Data Engineer

About MoonPay

Hi, we’re MoonPay. We’re here to onboard the world to the decentralized economy by making digital money move as universally and effortlessly as the internet.

Why

Because crypto, stablecoins and blockchain aren’t just technologies. They’re tools for global financial empowerment. They give people and businesses more control over their money, their digital assets, and their future, opening access to legacy financial systems that have been out of reach for many.

What we do MoonPay is a unified payments platform for digital currency. We make it easy for anyone, anywhere, to buy, sell, swap and pay in digital currencies as easy as sending an email. That simplicity is intentional, our focus is reducing complexity so people can participate confidently, without needing to be crypto experts. We power the entire flow between fiat and crypto end to end, with compliance, identity checks, fraud prevention, and settlement all built in. This end-to-end approach reflects how we work internally: with accountability, rigor, and trust built into everything we ship.

Proven at scale

Trusted by over 30 million customers and over 500 ecosystem partners, our secure, enterprise-grade platform is driving mainstream crypto adoption worldwide. Behind those numbers are millions of real people and organizations relying on MoonPay every day.

We collaborate with innovative brands and projects to build secure, scalable solutions for a blockchain-powered future. This is an opportunity to help shape systems, not just scale them. And we’re committed to doing it right. Fully licensed in the U.S. and regulated across the UK, EU, Canada, and Australia, because trust and compliance are non-negotiable.

But we’re just getting started. We’ve launched a consumer app that makes crypto accessible, intuitive, and usable for everyone, and it’s growing fast. We’re iterating every day to make it the best it can be.

If you believe financial freedom should be for everyone. If you believe in building a fairer, more open financial system - we want you with us. To build systems that benefit all, we need contributions from all, regardless of background.

Come build the future of payments and the decentralized economy with MoonPay. Let’s make financial freedom and autonomy the new normal.

About the Opportunity ✍️

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