Finance Data Analyst — E-commerce

The role

You sit between the payments platform and the general ledger. Every day the app records roughly 500,000 orders, refunds, partial refunds and chargebacks across three payment providers. Your job is a daily reconciliation that ties provider settlements to recognised revenue, flags the breaks by cause, and a Power BI view the commercial team reads without asking you.

• Daily reconciliation: orders → settlements → bank, by provider and currency, with breaks classified
• SQL models over the transaction tables — incremental, tested, documented
• Refund and chargeback analytics: rate, lag, cause, and what it costs
• A Power BI model the commercial team owns the questions for — you own the numbers
• Python for the awkward parts: provider files that change shape, late settlements, FX

What we look for

• SQL you can defend line by line; window functions are not optional
• A finance mind — you know what "recognised" means and why a refund is not a negative sale
• Power BI modelling at scale: star schema, incremental refresh, measures that stay fast
• Nice to have: Python/pandas, a payments background, Fabric

What you'll be tested on

Two Data Dare datasets. Type an answer per question, and upload your solved Power BI file for each dataset — required, and what the reviewer reads. A Python notebook is optional and strengthens your profile. No deadline: submit when your work is complete. Datasets unlock when you apply.