Senior Officer Risk Data Management

Role: Senior Officer - Risk Data Management

Location: Abu Dhabi

Role purpose:

• Prepare derive and validate variables and datasets required for the development recalibration validation and implementation of Application Scorecards and Behavioural Scorecards.

• Provide the Risk Modelling team with accurate model-ready datasets for A-Score and B-Score development.

• Support accurate implementation of approved scorecards in production as part of the model implementation exercise.

• Automate BAU activities including ingesting multi-source data in an agreed format and loading it into the Azure/CML database.

• Develop SAS-based automation on the existing SAS Risk Stratum engine and deploy ad-hoc changes as requirements arise.

• Coordinate with Model Development Model Validation Business and IT teams to resolve data and implementation issues escalating where required.

• Run the existing SAS suite that loads LGD model data including loss recovery and post-default event data from source systems.

• Prepare data for CBUAE regulatory reporting and BRF requirements.

Key accountabilities of the role:

Scorecard Variable & Data Preparation:

• Prepare derive and validate variables and analytical datasets required for Application and Behavioural Scorecard development recalibration validation and implementation.

• Interpret approved business definitions model specifications and variable logic and convert them into controlled SAS and SQL programs.

• Consolidate source data perform data-quality checks and deliver complete datasets in the agreed structure and format.

• Investigate data gaps anomalies and reconciliation differences and coordinate their resolution with relevant stakeholders.

• Prepare data for CBUAE regulatory reporting and BRF requirements.

Model Implementation & Testing:

• Support model implementation by providing accurate insights into the underlying data and model logic.

• Perform unit testing system integration testing user acceptance support reconciliation and parallel-run validation.

• Deploy SAS models to the IFRS 9 risk engine using SAS DI jobs and benchmark results across the Development and UAT environments.

SAS Engineering & Data Management:

• Develop maintain and optimise SAS and SQL programs used for variable preparation scorecard implementation and recurring data processing.

• Support model deployment using SAS Data Integration SAS Studio and related SAS platforms.

• Develop controlled data pipelines and maintain data lineage between source systems SAS processes modelling databases and downstream solutions.

• Execute relevant business-as-usual activities and provide technical support for implemented models.

Automation & Continuous Improvement:

• Automate recurring data preparation validation reconciliation and model implementation activities.

• Review and optimise existing SAS implementation code to improve efficiency maintainability and control.

• Identify and implement SAS-based automation opportunities to reduce manual intervention and support the Provisioning CBUAE Sup-Tech Risk Model BRFs.

• Support other risk model implementation and regulatory data automation activities where required.

Specialist skills / technical knowledge required for this role:

• Strong hands-on expertise in SAS programming SAS Data Integration SAS Studio and SQL.

• Practical experience preparing and deriving variables for Application and Behavioural Scorecards.

• Experience implementing scorecards or risk models including testing reconciliation and production deployment.

• Good understanding of data quality data lineage reconciliation model implementation controls and documentation.

• Ability to review and optimise SAS code and develop efficient controlled data pipelines.

• Working knowledge of relational database management systems and Oracle.

• Exposure to Python or SAS for analytics and automation.

• Strong analytical problem-solving documentation communication and stakeholder-management skills.

Previous experience required (if any):

• Minimum 35 years of relevant experience in SAS development risk data management scorecard/model implementation analytics or automation within banking or financial services.

• Demonstrated hands-on experience preparing variables and datasets for Application or Behavioural Scorecards.

• Experience translating approved model specifications into SAS- or SQL-based implementation logic.

• Experience working with Model Development Model Validation Business and IT stakeholders on controlled production implementation.

• Experience maintaining data-quality reports lineage implementation documentation and test evidence.

• Experience automating recurring modelling data-management or risk-reporting processes.

• Experience with SAS Viya Python SAS MRM would be an advantage.