Embedded finance now shows up inside checkout flows, payroll platforms, and lending apps that most users never think of as financial products at all. Someone still has to make sure the numbers behind those features are correct.
Take a buy-now-pay-later option built into a checkout page. Every approved installment plan generates a small chain of transactions across at least two ledgers, and if those ledgers drift apart even slightly, the mismatch compounds fast. Finding that drift before month-end close, not after, is a meaningful part of what this role is actually for.
This position is fully remote and open worldwide. There is no office tied to the role and no location requirement; you can do this work from anywhere with a stable internet connection. The companies hiring for roles like this one are often building financial features on top of a non-financial product, which means the analyst work sits a step removed from a traditional bank but the accuracy standard does not change at all.
The line between embedded finance and traditional banking work is mostly about where the product lives, not how carefully the numbers get checked. A payroll app offering earned-wage access still needs its transaction records to tie out exactly, the same as a bank would demand of its own ledgers.
You will analyze financial data and transactions connected to embedded finance products, looking for patterns that do not add up. Reconciliation is a regular part of the job, not an occasional task, and reports need to be clear enough for people outside finance to read and act on. Some weeks are steady and routine; close weeks are not, and the pace picks up accordingly.
A recurring part of the job is explaining a number to someone who is not a finance person, such as a product manager asking why a specific transaction fee line moved. Being able to answer that clearly, without jargon, is as much a part of the role as the analysis itself.
Forecasting work tends to arrive in bursts around planning cycles, and it draws on the same reconciliation habits used the rest of the month. A forecast built on ledgers that are not fully reconciled is really just a guess with a spreadsheet attached to it.
A bachelor's degree in finance, accounting, or economics is the baseline education requirement. Two years of experience analyzing financial transactions or working in a similar analyst role is expected, along with genuine comfort in spreadsheets and financial software rather than a passing familiarity with them. People who do well here tend to notice small inconsistencies before they turn into larger problems, and they are comfortable saying so even when the fix is inconvenient for someone else's timeline.
This is not a role for someone who wants to just run a report and move on. The useful part of the job happens after the report is generated, when a number looks slightly wrong and someone has to decide whether to chase it down.
Candidates coming from a traditional accounting background sometimes worry the fintech side of this role requires deep engineering knowledge. It does not. What it requires is comfort learning a new product's mechanics quickly enough to know which transactions should exist and which ones look out of place.
These are required for the role:
Useful but not required to apply:
Candidates who have pulled their own data from a database, rather than always waiting on someone else to export it, usually have an edge. Modeling skill matters most when a product team asks a hypothetical, such as what happens to reserve requirements if a lending feature triples its volume; a model built well enough to answer that quickly, without a week of rework, is worth more than one built purely for a quarterly report.
Analytical thinking, as a phrase on a job posting, tends to mean very little. In practice here it means sitting with a set of numbers that almost, but not quite, reconcile, and being willing to trace the difference back through several systems instead of writing it off as immaterial.
The role pays 90,000 dollars annually, full-time. Remoteroles posts embedded finance roles like this one from companies building financial products into other companies' platforms, which puts the work close to a lot of current fintech growth. Beyond salary, the package includes:
Bonus structure is tied to individual and team performance rather than a flat annual figure, so the details get spelled out during the offer stage once you know exactly which team you would be joining.
Most collaboration happens asynchronously through shared spreadsheets, dashboards, and a messaging tool, with scheduled check-ins for month-end close and reporting deadlines. You will need some overlap with your team's core hours to join those close-of-books conversations, but day-to-day analysis work can happen on your own schedule outside of that window. Expect a short daily or near-daily written update during close week, and a lighter cadence the rest of the month.
Outside of close week, the rhythm settles into something closer to independent research work, punctuated by a standing team meeting to review open items. Nobody expects you to be reachable around the clock, but during a reconciliation crunch, response time on flagged discrepancies does matter.
Send in your resume along with any examples of reconciliation or reporting work you are comfortable sharing. Shortlisted candidates typically hear back within two weeks and move into a short skills conversation before a final interview, often built around a sample reconciliation problem you would walk through live.