Job Description

About Reap

Reap is a global financial technology company headquartered in Hong Kong, with teams across multiple countries. We bridge traditional finance and stablecoins to make money movement more efficient for businesses worldwide — through stablecoin-powered corporate cards, payments, and expense management tools, and through APIs that let businesses embed finance into their own products, from issuing Visa cards to moving money across borders. Reap is backed by leading investors and is building the future of borderless, stablecoin-enabled finance.

About the Role

Reap’s Data & Analytics team is building a self-service analytics platform with three access paths governed SQL on Snowflake, Metabase dashboards, and AI-assisted answers through Claude all reading from a single governed semantic layer, so every team gets consistent numbers whichever path they take.

As a Senior Analytics Engineer, you’ll own a set of Reap’s business domains end to end from the underlying data and definitions to the dashboards to the stakeholder relationships, with no handoffs in between.

The title is deliberate: we’re not hiring someone to answer questions one at a time, but someone who builds the system that answers them permanently and who spends the bulk of their time on the analysis that moves the business: pricing and cost-saving analyses, forecasting, client deep-dives, and revenue-generating proposals, in close partnership with key business team stakeholders.

You’ll surface revenue opportunities and margin-improvement levers through structured analyses of pricing, discounts, product mix, client profitability, and usage patterns, turning data into actionable commercial recommendations.

You’ll report to our Analytics Lead under the Data & Analytics functional team and work day-to-day with upstream engineers, data engineers, fellow analytics engineers, and data product managers in a distributed, async-friendly team.

What You'll Do

• Own your domains end to end - a single named owner from the data to the dashboard to the conversation with the stakeholder asking the question.
• Deliver value-added analysis for your domains: pricing and cost-saving analyses, forecasting, client deep-dives, and proposals that shape commercial decisions.
• Identify and quantify revenue opportunities and growth levers such as pricing optimization, upsell/cross-sell potential, retention drivers, and margin expansion and translate them into clear recommendations for business owners.
• Partner with your domains’ business teams as embedded decision support in the meetings and shaping the decisions, not working a ticket queue.
• Turn recurring questions into permanent answers: every repeat request becomes a new column, dashboard, or documentation entry asked once, never again.
• Design and maintain your domains’ vetted views and metric definitions in our Snowflake semantic layer the definitions all three access paths read from.
• Build and own your domains’ Metabase dashboards, and keep their numbers trustworthy: validated, reconciled against Finance-owned figures, and debugged to root cause when they drift.
• Run your domains’ enablement documentation, training sessions, weekly office hours, and Slack support so stakeholders self-serve the routine questions.
• Collaborate with Data Engineering on ingestion and modeling decisions, and with Data Product Managers on priorities and rollout.

What We're Looking For

• 8+ years in analytics engineering or analytics roles, including end-to-end ownership of a business domain’s data, metrics, and reporting.
• Strong analytical judgment: you can take an ambiguous business question and drive it to a structured, decision-ready answer.
• A stakeholder partner’s instincts comfortable embedded with commercial, finance, or risk teams, and credible in their conversations.
• Expert SQL, with solid dimensional modeling fundamentals and experience building or maintaining semantic or metrics layers.
• Hands-on experience with a modern cloud data warehouse and BI tooling we use Snowflake and Metabase.
• The rigor financial data demands: validation, reconciliation, and chasing discrepancies to root cause.
• A teacher’s communication skills: clear documentation, and the ability to train non-technical stakeholders to self-serve.
• Proven ability to generate revenue and opportunity insights: you’ve previously built pricing, profitability, or growth analyses that directly influenced commercial strategy, investment decisions, or client proposals.

Nice to Have

• Fintech, payments, or card-issuing domain experience (e.g. card transactions, settlement, FX).
• Experience with dbt or similar transformation and testing frameworks.
• Python or another scripting language for validation and automation.
• Experience with / or strong interest in AI-assisted analytics: LLM tooling, skill/prompt design, or text-to-SQL over governed data.
• Experience contributing to data governance: access models, PII handling, naming standards.
• Track record of building revenue or opportunity dashboards, pricing models, or commercial scorecards that business teams use to drive growth and margin improvement.

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