Data Analyst

About the Role

The Operational Risk Data Analyst II is accountable for the consolidation, verification, analysis and reporting of operational risk data across the bank — producing high-quality risk insights, regulatory submissions, operational loss analysis and forward-looking intelligence to support effective decision-making. The role acts as a link between data analytics and operational risk management, enhancing the bank's risk data landscape, strengthening prudential reporting capabilities and building a data-driven risk insights capability within the function.Operational risk data flows in from multiple directions — loss events, RCSAs, KRIs, audit findings, near misses.

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 and decision-making.
• At least 5 years experience in data analysis in a risk management or financial services environment
• Operational risk frameworks
• RCSA, KRI, loss data and scenario analysis
• Data governance and data quality principles
• Working with large, fragmented datasets
• ideally in a risk environment
• Designing impactful visualisations that communicate risk insights effectively
• Experience In using advanced data analysis tools and software (e.g., SQL, Python, R, Tableau, Power BI).
• Experience in performing complex data analysis and statistical modelling.
• Experience working with, guiding and providing subject matter expertise to cross-functional teams (e.g., finance, marketing, IT) to understand business needs and provide data-driven insights.
• Advanced proficiency in writing complex SQL queries, optimizing query performance, and working with large datasets.
• Expertise in advanced Excel functions, including macros and VBA.
• Proficiency in creating advanced visualizations and dashboards using tools like Tableau, Power BI, or similar.
• Advanced skills in Python or R, including data manipulation libraries (e.g., pandas, numpy) and data visualization libraries (e.g., matplotlib, seaborn).
• Strong understanding of statistical methods and their application in financial data analysis.
• Understanding of predictive analytics techniques and their application in financial analysis.
• Knowledge of data modelling techniques to structure and organize data effectively.
• Understanding of risk analysis methods and their application in financial services.

Requirements

• Bachelor's Degree in Analytical/Data/Technical or Other
• Clear criminal and credit record