Job Description & Details
This is a heavy-hitting data analyst role focused squarely on payment systems, enterprise data reconciliation, and data governance. You will spend a good chunk of your time knee-deep in CHIPS, wire-payment data, and AWS pipelines making sure financial ledgers line up and data quality issues are squashed before they hit leadership. If you enjoy deep-dive SQL investigations and building out reliable financial metrics rather than just churning out basic vanity charts, this one's worth looking at.
What You'll Actually Be Doing
Your day-to-day will revolve around untangling complex payment data, running heavy SQL reconciliations, and investigating discrepancies between various financial sources. You will work closely with stakeholders to define business rules, document data lineage, and build out robust dashboards using tools like Amazon QuickSight or Power BI. When payment or data quality anomalies pop up—which they inevitably will in high-volume wire systems—you own the investigation from root-cause analysis all the way to permanent resolution.
The Core Tech Stack
SQL is your bread and butter here; you need to be lightning-fast at writing complex queries, joins, and aggregations across massive datasets without breaking a sweat. AWS is the cloud home, so comfort querying data sitting in S3 via Athena is mandatory, along with BI chops in Amazon QuickSight, Power BI, or Tableau. On top of the tooling, domain familiarity with financial networks like CHIPS, Fedwire, or ISO 20022 message formats will make or break your ability to ramp up quickly in this environment.
Interview Expectations
Expect the hiring team to grill you on your reconciliation methodology, likely asking how you would handle a scenario where a massive batch of wire-payment transactions fails to reconcile between a raw ingestion table and a downstream ledger. They want to see a systematic approach to root-cause analysis, tracing data lineage, and working with cross-functional teams under pressure. You should also be ready for a live SQL test focusing on window functions, self-joins, and data validation techniques.
Application Advice
With a strict 9+ year requirement, make sure your resume immediately highlights your seniority in data analysis within the banking, payments, or fintech sectors. Do not just list your tools; weave in specific metrics around the scale of the financial data you have managed, your experience with data governance, and explicit examples of how your SQL reconciliation work saved the day. Drop exact keywords like CHIPS, Fedwire, data lineage, root-cause analysis, Amazon Athena, and QuickSight straight into your experience bullets to clear the ATS parsing filters without issues.