Operational Risk Data Analyst II

About the role

The Operational Risk Data Analyst II consolidates, verifies, analyses and reports operational risk data across the bank, producing risk insights, regulatory submissions, operational loss analysis and forward-looking intelligence. The role links data analytics with operational risk management, strengthens prudential reporting and makes risk visible, measurable and actionable for decision-makers from business units to ExCo and the Board.

Key responsibilities

• Manage, govern and enhance operational risk data, ensuring quality, accuracy and alignment with BCBS 239 principles.
• Analyse risk trends, loss events and emerging risks to identify key drivers and control gaps.
• Develop dashboards, management information and actionable insights for senior leadership and risk committees.
• Support regulatory and prudential reporting, including Basel, ICAAP and Banks Act requirements.
• Automate and improve risk reporting and data processes using SQL, Python, Power BI or similar tools.
• Partner with business and Operational Risk teams to strengthen data-driven risk management.

Required profile

• At least 5 years of data analysis experience in a risk management or financial services environment.
• Knowledge of operational risk frameworks (RCSA, KRI, loss data, scenario analysis), data governance and data quality principles.
• Experience designing visualisations that communicate risk insights and working with large, fragmented datasets.
• Experience guiding cross-functional teams (finance, marketing, IT) and providing data-driven insights.
• Bachelor's degree in an analytical, data or technical discipline or other.
• Clear criminal and credit record; applications considered in line with employment equity goals.

Required skills

• Advanced SQL, including complex queries and performance optimisation.
• Advanced Excel, including macros and VBA.
• Python or R, with libraries such as pandas, numpy, matplotlib and seaborn.
• Tableau, Power BI or similar for dashboards and visualisations.
• Statistical methods, predictive analytics and data modelling.