Tools & Technologies: Databricks, Azure, SQL, Python, Microsoft Excel, Jira, DevOps, Git
ABOUT CLIENT
Our client is a reputed European multinational investment bank operating across multiple business units in capital markets and financial services. The bank deployed an internal Investment and Capital Performance Management (ICPM) reporting portal to automate the generation of critical regulatory and financial reports — including quarterly Income Statements, forward-looking Projections, and FRY-14 regulatory compliance reports. The platform handles highly complex statistical computations, counterparty credit calculations, and multi-layered financial data aggregation that directly supports senior management decision-making and regulatory submission obligations.
KEY REQUIREMENTS
- End-to-end functional and data validation testing of the investment banking reporting portal — covering Income Statement generation, quarterly financial projections, and FRY-14 regulatory compliance reports across all business units
- Creation of a structured Test Strategy and Test Plan tailored for complex financial report generation — defining scope, risk-based test approach, entry/exit criteria, and data validation techniques for investment banking reporting scenarios
- Rigorous validation of financial report calculations driven by quarterly reporting data, static reference data sets, and counterparty credit exposure figures — ensuring 100% numerical accuracy across all regulatory and management reports
- Structured training and enablement of QA professionals in investment banking domain knowledge, regulatory reporting concepts, and test execution techniques — building an independent, self-sufficient testing team for ongoing portal quality assurance
- Systematic defect reporting, priority-based bug tracking, and development team follow-up using Jira and Azure DevOps — ensuring all identified report discrepancies and calculation errors were resolved before regulatory submission deadlines
KEY CHALLENGES
- Validating report outputs driven by multi-layered statistical calculations, complex financial expressions, and cascading data dependencies — where even minor upstream data discrepancies could propagate into significant errors in regulatory report figures
- Managing and precisely controlling large volumes of quarterly financial data, static reference datasets, and counterparty credit exposure records required for test scenario setup — demanding meticulous test data management strategies to ensure repeatable and accurate report validation
- Accurately testing FRY-14 regulatory reports demanded deep specialist knowledge of investment banking compliance frameworks, capital adequacy standards, and supervisory reporting requirements — a domain expertise gap that required significant upfront learning and collaboration with subject matter experts
- Onboarding QA professionals with limited exposure to investment banking domain concepts, regulatory reporting workflows, and the Databricks and Azure-based technology stack — requiring a structured knowledge transfer programme to bring the team to productive testing capacity within tight project timelines
SOLUTION PROVIDED
- Performed in-depth requirement analysis of investment banking reporting specifications and regulatory guidelines, then authored a comprehensive Test Strategy and Test Plan covering testing scope, risk-based prioritization, data validation approach, and acceptance criteria for all report types
- Authored detailed, traceable test cases for all financial reporting scenarios — covering Income Statement line items, projection calculations, FRY-14 regulatory data points, and counterparty credit figures — and executed them systematically across multiple quarterly data cycles
- Leveraged SQL, Python scripting, and Databricks notebooks to independently validate complex report calculations against source financial data — cross-referencing portal outputs with raw data in Azure data layers to identify and evidence numerical discrepancies at the source level
- Managed end-to-end defect lifecycle using Jira and Azure DevOps — logging detailed bug reports with data evidence, assigning severity and priority classifications, coordinating developer follow-up, and tracking all report calculation defects through to verified resolution
- Designed and delivered a structured training programme for QA professionals covering investment banking domain concepts, regulatory reporting terminology, SQL-based data validation techniques, and Databricks tooling — enabling the team to independently own and execute regression test cycles
- Collaborated closely with business stakeholders, investment banking domain experts, and compliance teams to cross-verify regulatory accuracy and business logic correctness of all generated reports — ensuring every Income Statement, financial projection, and FRY-14 submission met both internal governance standards and external regulatory obligations