Data Analyst

About Paxos

Today’s financial infrastructure is archaic, expensive, inefficient and risky — supporting a system that leaves out more people than it lets in. Paxos is rebuilding it, on a mission to open the world’s financial system to everyone by enabling the instant movement of any asset, any time, in a trustworthy way. For over a decade, they've built blockchain infrastructure that tokenizes, custodies, trades and settles assets for the world’s leading financial institutions, like Mastercard, Visa, Robinhood, and PayPal.

About The Team

The Analytics team at Paxos is central to data-driven decision-making. This dedicated group of analysts and data professionals transforms raw data into insights that guide product, operations, risk, and commercial strategies. The team is passionate about building trustworthy metrics, intuitive dashboards, and clear analyses to help stakeholders move faster and with more confidence. They partner closely with teams across the company, leveraging deep understanding of the business and products to solve complex problems and unlock new opportunities through data.

About The Data Analyst Role

As a Data Analyst at Paxos, you will own analytics for Paxos stablecoins and rewards initiatives. You will combine strong SQL, dashboarding, and analytical skills with a product mindset to help teams make better decisions, design smarter incentives, and meet regulatory and reporting obligations. You’ll work closely with Product, Engineering, Finance, and Account Management to turn complex data into clear, actionable insights in a lean, high‑ownership environment.

What You’ll Do

• Own the analytics for Paxos-issued tokens and Rewards as a product, including defining core metrics, building dashboards, and creating analyses that inform product, rewards, and network strategy.
• Translate ambiguous business questions from Product, Engineering, Finance, and Account Management into well‑scoped data problems and deliver clear, decision‑ready outputs (dashboards, analyses, memos, reports).
• Partner with the data engineering team and cross-functional stakeholders to shape and improve the underlying data models in Snowflake and dbt, ensuring the stablecoin and rewards domains are well‑modeled, trusted, and documented.
• Use AI‑powered tooling (e.g., Snowflake native AI, Cortex, Claude/Claude Code, Streamlit apps) to accelerate analysis, improve model quality, and prototype new analytics workflows.
• Contribute to regulatory reporting, audits, and exam requests as part of the team’s BAU responsibilities, ensuring accuracy, consistency, and timely delivery.

About You

• 2-5 years of experience in data and analytics roles (e.g., Data Analyst, Analytics Engineer, Technical Product Analyst), ideally in a product or financial/fintech environment.
• Advanced SQL proficiency, including querying large, complex datasets and reasoning about data model design - not just editing or copy‑pasting existing queries.
• Hands-on dashboarding and data visualization experience with tools such as Looker, Metabase, or Tableau, including designing new dashboards and metric frameworks rather than only maintaining existing ones.
• Strong analytical and quantitative skills, with practical experience in metrics development, data modeling concepts, and basic statistics, working on top of a modern warehouse + modeling stack (e.g., Snowflake + dbt or equivalent).
• Comfort with Git-based workflows for managing analytics code and assets (SQL, dbt models, dashboard definitions), including branching, pull requests, code review, and CI/CD in the data stack.
• Demonstrated ability to leverage AI in analytics workflows, ideally including Snowflake-native AI (e.g., Cortex), Claude Code, and Streamlit apps to “vibe code” dashboards or accelerate analysis and modeling work.
• Excellent written and verbal communication skills, with the ability to explain complex technical and quantitative concepts to non-technical stakeholders and tailor messaging to product, engineering, finance, and account management audiences.
• Strong stakeholder and requirements-gathering skills: able to collect, challenge, and distill business needs into clear, actionable deliverables (dashboards, analyses, reports), including regulatory reporting, audit, and exam requests as part of BAU work.
• A growth mindset and high adaptability: eager to learn, comfortable with ambiguity, and willing to experiment with new tools and approaches (including AI), rather than being rigid or “set in your ways.”

Key skills/competency

• Data Analysis
• SQL
• Snowflake
• dbt
• Dashboarding
• Data Visualization
• Fintech Analytics
• Product Analytics
• AI in Analytics
• Regulatory Reporting