ICB Risk Data Analytics – Risk Modelling Senior Associate

Job summary As a Senior Associate in the International Consumer Banking Risk Data Office, you will assume ownership of the current set of risk modeling data models and views, including the setup and maintenance of modeling-specific data tables. You will create and maintain dbt transformation rules, blueprint files, and data dictionaries; automate key processes; maintain Apache Airflow workflows supporting the ongoing performance monitoring process; and deliver required data reporting. You will collaborate with model risk stakeholders and technology teams to translate monitoring requirements into clear data requirements, transformation logic, and consumable datasets. Job responsibilities Collaborate with model risk stakeholders (for example, model owners, validators, ongoing performance monitoring leads, risk data owners, and technology teams) to translate ongoing performance monitoring and model monitoring requirements into clear data requirements, transformation logic, and consumable datasets. Design, build, and maintain scalable Amazon Web Services-based data lake tables and curated datasets using dbt and Trino, aligned to Medallion architecture (Bronze/Silver/Gold) and best-practice modeling approaches for monitoring, analytics, and reporting. Create and maintain dbt transformation rules and supporting governance artifacts, including developing blueprint files for attribute tagging (for example, ongoing performance monitoring metrics, thresholds, segmentation attributes, critical data elements, and sensitivity/classification where applicable) and maintaining data dictionaries and business/technical definitions, along with end-to-end data lineage documentation and logic traceability to model monitoring requirements. Analyze, onboard, and integrate new data sources required for ongoing performance monitoring (model inputs, outputs, outcomes, performance labels, and operational/process data), including source profiling, quality assessment, and mapping to monitoring metrics. Implement, govern, and operationalize data quality standards for ongoing performance monitoring datasets (completeness, accuracy, timeliness, uniqueness, and referential integrity) using dbt expectations and reusable dbt macros to support consistency, maintainability, and auditability. Build and maintain Apache Airflow workflows to automate end-to-end ongoing performance monitoring data processing, including orchestration, scheduling, dependency handling, backfills, monitoring/alerting, and operational runbooks. Automate key processes supporting model monitoring (for example, metric dataset refreshes, segmentation datasets, challenger/benchmark feeds, and monitoring aggregations) to improve control, reduce manual effort, and increase scalability. Execute and support data migrations relevant to model risk monitoring (platform upgrades, source changes, and dataset redefinitions), ensuring robust planning, validation, reconciliation, and controlled delivery aligned to governance requirements. Operate key governance controls across data quality, data protection, data use, and data retention/destruction, aligned to Basel Committee on Banking Supervision (BCBS) 239 principles and risk-management expectations for monitored, controlled data products. Ensure operational excellence for ongoing performance monitoring data products, including production readiness, incident triage, root-cause analysis, remediation, and continuous improvement to reduce defects and improve monitoring sustainability. Required qualifications, capabilities, and skills Hold a Bachelor’s degree in a relevant field (for example, Computer Science, Engineering, Information Systems, or Statistics) and have 7+ years of experience in data engineering, data management, analytics, or related roles. Demonstrate a strong understanding of model risk concepts and the needs of ongoing performance monitoring, with the ability to translate monitoring and control requirements into resilient data models and pipelines. Apply advanced SQL skills (window functions, joins, common table expressions, and complex aggregations), including performance tuning and building scalable analytical datasets. Bring strong hands-on experience with Amazon Web Services data platforms and modern engineering tooling, including dbt, dbt expectations, Trino, Python, and GitHub (or equivalent software development lifecycle tooling). Show proven experience building scalable dbt models using incremental processing techniques and designing reusable dbt macros to improve consistency, maintainability, and auditability. Communicate effectively across Risk and Technology teams, with experience working in Agile environments. Preferred qualifications, capabilities, and skills Have experience with model monitoring datasets and patterns such as performance metrics, drift/stability monitoring, segmentation, thresholds, and outcomes/labels alignment (where applicable to the model type). Bring experience with Python data libraries (for example, pandas and numpy) and descriptive statistics concepts supporting monitoring and data quality analysis. Demonstrate proficiency with Jira, Confluence, and Microsoft Office.