Senior Officer - Risk Data Management
At a glance
Senior Data Architecture & Governance role at ADIB Candidate Experience site. United Arab Emirates · full-time.
Pay not stated Data Architecture & Governance salary
dataroles summary, based on the employer's posting.
What you'll do
- Build and quality-check scorecard inputs, then work with stakeholders to resolve missing data and mismatched totals
- Translate approved model rules into SAS and SQL programs that prepare scorecard data
- Test model changes, compare results across Development and UAT, and help move approved models into production
- Automate recurring data preparation, checks and reconciliations for modelling and regulatory reporting
- Run SAS routines for LGD loss, recovery and post-default records, while tracking data lineage across systems
What you bring
- At least three years of relevant SAS, risk-data, model implementation, analytics or automation work in banking or financial services
- Hands-on SAS and SQL capability, including preparing scorecard variables and analytical data
- Experience turning model specifications into implementation logic and supporting tests, reconciliations and production deployment
- You have worked with model, business and IT teams and kept data-quality reports, lineage records and implementation evidence
Who this fits
This senior role suits you if you have practical SAS experience supporting scorecard or risk-model implementation and recurring automation. You’ll coordinate with modelling, validation, business and IT teams. The role is based in Abu Dhabi.
From the employer
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 3–5 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.
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