Develop risk data and reporting: Identify and map relevant data sources, using tools such as Snowflake to build repeatable datasets for KRIs across operational, credit, fraud, AML, people, capital, liquidity and other risk categories.
Partner with risk stakeholders: Work closely with the Head of Enterprise & Operational Risk and risk owners across the business to understand their priorities, clarify reporting requirements and translate risk management needs into practical analytical solutions.
Analyse and monitor risk indicators: Use SQL and Python to investigate trends, test assumptions, identify anomalies and understand changes in risk exposure. Help risk owners interpret findings and determine where further investigation or action may be needed.
Support KRI definitions and thresholds: Work with risk stakeholders to validate indicator definitions, assess data quality and analyse historical and current performance to inform threshold calibration. Help ensure indicators provide meaningful insight into the risks they are intended to monitor.
Build dashboards and management reporting: Develop clear, accessible dashboards using Tableau, Power BI or similar tools, supporting monthly ERMF reporting and management oversight.
Automate risk reporting with AI: Build and improve AI-enabled workflows that bring together internal data and relevant external signals, including regulatory updates, market developments, news and emerging threats. Help generate evidence-based reporting drafts for review by the Head of Enterprise & Operational Risk and CRO.
Develop early-warning capabilities: Help design and test approaches to monitor KRI movements and operational data throughout the month, highlighting unusual patterns and potential emerging risks with clear supporting evidence for investigation.
Support risk technology and integration: Translate ERMF and reporting requirements into practical data, integration and workflow specifications for Teya's governance, risk and compliance (GRC) solution, partnering with Risk, Data and Technology colleagues on implementation.
Improve data quality and governance: Document data sources, definitions, lineage, refresh frequencies, validation checks and limitations to ensure reporting is consistent, explainable and reliable.
Drive continuous improvement: Identify opportunities to simplify reporting, improve analytical processes and strengthen how risk information is used across the business.
What you'll bring
Experience in a data analytics, risk analytics or similar analytical role within a risk function, ideally covering enterprise, operational, credit, fraud or financial crime risk.
A practical understanding of how risk is identified, assessed and monitored, including how risk appetite, controls, risk drivers, KRIs and thresholds connect.
Strong business partnering skills, with the ability to work closely with risk owners and non-technical stakeholders, understand their needs and translate them into meaningful analysis, reporting and monitoring solutions.
Working knowledge of SQL and Python for data analysis and practical automation. You don't need to be a software engineer, but you should be comfortable using these tools, including with AI assistance, and validating the accuracy of the results.
Experience building dashboards and management reports using Tableau, Power BI or similar visualisation tools.
Strong analytical judgement, with the ability to challenge assumptions, investigate unexpected results, assess data quality and communicate clearly what the data does—and does not—show.
Experience working with data from multiple sources and improving the reliability, consistency and usability of analytical outputs.
Hands-on curiosity about current AI models and AI-enabled workflow automation, with the ability to build and improve workflows that support data gathering, analysis and risk reporting.
A structured, pragmatic approach to problem-solving, with the confidence to work through ambiguity and take ownership of deliverables.
Useful, but not essential
Experience in financial services, payments, fintech or another regulated environment.
Exposure to enterprise risk management frameworks and risk appetite frameworks.
Experience working with Snowflake or another cloud data warehouse.
Familiarity with data pipelines, APIs, workflow orchestration or system integrations.
Exposure to governance, risk and compliance (GRC) platforms.
Experience with statistical analysis, anomaly detection, forecasting or early-warning indicators.
Experience using external regulatory, market or threat intelligence to support risk monitoring.
Familiarity with AI agents, large language models (LLMs) or AI-enabled workflow automation tools.
What success looks like
Trusted risk data: KRI datasets are accurate, consistent, well-documented and refreshed at the required frequency.
Meaningful risk insight: Analysis helps risk owners understand changes in risk exposure, investigate anomalies and make informed decisions.
Effective business partnering: Risk stakeholders have confidence in the data, reporting and analysis provided, and their requirements are translated into practical solutions.
Clear management reporting: Monthly ERMF reporting is timely, evidence-based and increasingly automated.
Earlier identification of emerging risks: Explainable monitoring highlights significant movements and potential issues for investigation.
Reliable AI-enabled workflows: Automation reduces manual effort while maintaining accuracy, traceability and appropriate human review.
Stronger risk data foundations: Data definitions, quality checks and reporting processes support the continued development of Teya's ERMF and GRC capabilities.