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We are looking for a technical problem-solver to join our Global Regulatory Reporting Analytics Team. In this role, you will act as the "Gatekeeper" of our regulatory data. You will be responsible for two critical functions: (1) Proactive Impact Analysis, where you assess how upstream system changes (product updates, migration, schema changes) will affect our regulatory reports, and (2) Reactive Root Cause Analysis, where you investigate data anomalies reported by the reporting team. You will sit at the intersection of Technology and Finance. You must be fluent in SQL to dig into the data and fluent in Financial Products to understand the business context.
Job Responsibility:
Monitor and review upcoming changes in upstream source systems (e.g., changes to General Ledger, Data Warehouse, Product Processors, or Reference Data)
Analyze the "Blast Radius" of these changes: Use data mapping files and SQL to determine if an upstream attribute change (addition/removal/logic change) impacts specific regulatory report lines
Conduct "content analysis" to verify if the nature of the data is changing, even if the schema remains the same
Investigate "perceived data issues" raised by the Regulatory Reporting team
Execute complex SQL queries to trace data lineage back to the source
Determine the verdict: Is this a true upstream Data Quality (DQ) issue, a logic gap in our transformation layer, or a valid business event?
Prevent false alarms from reaching the technology development team by filtering out non-issues
Translate findings into actionable JIRA tickets
Define test cases for User Acceptance Testing (UAT) to ensure upstream changes do not cause regression in reporting
Act as the translator between the Reporting Team (who speak in "Reg Rules") and the Technology Team (who speak in "Code/Schema")
Present impact assessments to stakeholders to get sign-off on upcoming changes
Requirements:
Advanced SQL: comfortable writing complex queries from scratch (Joins, Window Functions, Case Logic) to profile data and validate hypotheses
Data Analysis: Ability to compare large datasets (Source vs. Target) to identify variances
Domain Knowledge (Banking or Trading): Familiarity with Lending products (Mortgages, Revolving Credit, Term Loans), Deposits, and General Ledger concepts
Familiarity with Capital Markets products (Derivatives, Securities, Repos) and trade lifecycles
Understanding of how product data translates to financial reporting (e.g., specific attributes like Maturity Date, Interest Rate, Counterparty Type)
Process & Tools: Experience with JIRA for defect tracking and requirements management
Ability to read and interpret Data Mapping Documents (Source-to-Target mappings)
Experience: 6+ years
Nice to have:
Experience with specific regulations (e.g., FR Y9C, FFIEC 031, FR Y-14, CCAR, FFIEC 009/009A, LCR, 2052a, or other Regulatory Reporting)
Python scripting skills for automating data comparison tasks
Previous experience in a "Data Quality," "Data Governance," or "System Integration" role