Staff Data Analyst, Engine X

Come outpace the ordinary and build the future of travel with us.

Your Mission

We are seeking a Staff Product Data Analyst to join the Engine X team. Engine X is Engine's corporate charge card — the first financial product Engine has launched and the first card built specifically on Engine's own supply relationships, pricing intelligence, and travel infrastructure.

Data is a core competency of Engine, and the Engine X team runs on it. In this role, you will be the analytical execution layer for a product that generates card transaction data, rewards activity, activation events, spend category breakdowns, and booking behavior at increasing volume. Your job is to make sure the team has accurate, well-structured, and timely analytical output, reliable dashboards, clean reporting frameworks, and clearly communicated findings, so that product managers, finance, and leadership can make decisions with confidence.

• →Build, maintain, and improve Engine X's core performance dashboards; covering card activation rates, spend volume, gross booking value, rewards utilization, spend category breakdowns, and cardholder retention.
• →Track and report on key Engine X metrics on a regular cadence, ensuring stakeholders across product, finance, and leadership have accurate and timely visibility into card performance.
• →Investigate data anomalies and surface findings clearly; when a metric moves unexpectedly, you diagnose it, document what you found, and communicate it before it becomes a question someone else is asking.
• →Write and maintain complex SQL queries to extract, clean, and aggregate data from Snowflake across card transaction logs, booking activity, rewards ledger data, and cardholder activation events.
• →Build and document reusable data models and reporting assets that the team can rely on — well-structured, well-named, and built to last beyond the person who created them.
• →Support A/B tests and product experiments by setting up the analytical framework, pulling results accurately, and presenting findings in a format that informs a clear decision.
• →Partner with senior analysts and product managers to scope analytical requests, understand what's being asked, and deliver work that's precise, complete, and ready to act on.
• →Collaborate with analytics engineering and data engineering to flag data quality issues, validate pipeline outputs, and ensure the underlying data powering Engine X reporting is reliable.
• →Develop and maintain clear documentation for the metrics and models you own, definitions, logic, known limitations, and refresh cadences, so the team always knows what the numbers mean.

• Bachelor's degree in Data, Economics, Statistics, Computer Science, Finance, or a related quantitative field.
• 7+ years of relevant experience in data analytics or a closely related analytical function, with demonstrated ability to produce clean, accurate, and well-structured analytical work.
• Solid SQL skills with experience writing complex queries — joins, aggregations, window functions — on large datasets; hands-on experience with Snowflake.
• Experience building and maintaining dashboards or reporting frameworks using HEX, Looker, Tableau, or a comparable BI tool.
• Attention to detail and a strong sense of ownership over the accuracy of your own work — you check your numbers, document your logic, and flag issues before they propagate.
• Clear written and verbal communication skills; able to present findings in a structured, easy-to-interpret format for non-technical stakeholders.
• Familiarity with A/B testing and experiment analysis — comfortable pulling results and applying basic statistical reasoning to interpret them correctly.
• Curiosity about the product and business context behind the data — you want to understand what you're measuring and why it matters, not just how to pull it.
• Experience in fintech, payments, card products, or financial services analytics is a plus.
• Familiarity with dbt or experience working alongside analytics engineering teams to understand and leverage data models is a plus.