Data engineer - regulatory reporting ( IFRS9, Basel)
Summary
Builds and maintains ETL pipelines in SQL, Python, and SAS to feed IFRS9 and Basel risk models, ensuring clean data for credit risk calculations and regulatory reporting.
Key Responsibilities:
1. ETL & Risk Data Engineering
- Design, develop and maintain scalable ETL pipelines using SQL, Python, and SAS
- Support datasets for default identification, post-default events, recovery, and exposure
- Implement incremental and full-load strategies ensuring no duplication or leakage
- Optimize SQL for large-scale distributed processing environments
- Integrate data from core banking, collections, GSAM, and external sources 2. IFRS9 & Credit Risk Data Management
- Translating IFRS9 methodology into technical data pipelines
- Support PD, LGD, EAD model data preparation
- Implement staging (Stage 1/2/3), default, and curing logic
- Handle recoveries, write-offs, restructures, and exposure calculations 3. Model Implementation & Validation Support
- Provision model-ready datasets for deployment
- Support feature engineering, segmentation, and backtesting datasets
- Perform reconciliation with developed/reference outputs and identify mismatches 4. Data Quality & Governance
- Develop DQ frameworks covering completeness, accuracy, and consistency
- Perform root cause analysis on data issues
- Ensure full data lineage and traceability 5. Risk Technology & System Integration
- Collaborate with IT for system integration and model deployment
- Support risk system migration and upgrades
- Perform testing for data migration and model accuracy 6. Automation & Reporting
- Automate regulatory and internal reporting processes
- Prepare datasets for dashboards and regulatory submissions
- Support reporting tools such as Power BI and BusinessObjects Preferred Tools & Technologies: SQL (Impala, Hive, Oracle), Python, SAS, Cloudera CDP, Power BI, SAP BO Core Domain Expertise: IFRS9, Basel II/III, PD/LGD/EAD modelling, credit risk data lifecycle Skills: data,etl,reporting