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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in data analytics and risk management, utilizing SQL and Python for data analysis and automation. Proficient in building dashboards with Tableau or Power BI, while ensuring data quality and effective communication of analytical insights.
Highest-signal resume keywords
SQL Data AnalysisPython AutomationDashboard DevelopmentRisk AnalyticsAI-Enabled Workflow Automation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data AnalyticsRisk AnalyticsStatistical AnalysisAnomaly DetectionForecastingKRI DefinitionData Quality AssessmentThreshold CalibrationData IntegrationWorkflow Specification
Soft Skills
Business PartneringAnalytical JudgementProblem-SolvingCommunicationOwnership
Tools & Technologies
SnowflakeTableauPower BIGRC PlatformsAPIs
Industry Keywords
Enterprise Risk ManagementOperational RiskCredit RiskFraud RiskFinancial Crime RiskRegulated EnvironmentFintechPayments
Tech Stack
Tools & technologiesCloudPythonSQLTableau
About the role
Key responsibilities & impact- Identify and map relevant data sources and build repeatable Snowflake datasets for KRIs across operational, credit, fraud, AML, people, capital, liquidity and other risk categories.
- Partner with the Head of Enterprise & Operational Risk and risk owners to understand priorities and translate reporting requirements into analytical solutions.
- Use SQL and Python to investigate trends, test assumptions, identify anomalies and analyse changes in risk exposure.
- Support KRI definition, data-quality validation and threshold calibration using historical and current performance.
- Build accessible dashboards and management reports using Tableau, Power BI or similar tools for monthly ERMF reporting.
- Build and improve AI-enabled workflows using internal data and external signals such as regulatory updates, market developments, news and emerging threats.
- Develop early-warning approaches to monitor KRI movements and operational data and highlight emerging risks with supporting evidence.
- Translate ERMF and reporting requirements into data, integration and workflow specifications for Teya's GRC solution.
- Document data sources, definitions, lineage, refresh frequencies, validation checks and limitations.
- Identify opportunities to simplify reporting, improve analytical processes and strengthen risk-information use.
Requirements
What you’ll need- 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.
- Practical understanding of how risk is identified, assessed and monitored, including risk appetite, controls, risk drivers, KRIs and thresholds.
- Strong business partnering skills and ability to translate risk-management needs into analytical solutions.
- Working knowledge of SQL and Python for data analysis and practical automation, including with AI assistance, and ability to validate results.
- Experience building dashboards and management reports using Tableau, Power BI or similar visualisation tools.
- Strong analytical judgement, data-quality assessment and ability to communicate analytical limitations.
- Experience working with data from multiple sources and improving reliability, consistency and usability of analytical outputs.
- Hands-on familiarity with current AI models and AI-enabled workflow automation.
- Structured, pragmatic approach to problem-solving and ownership of deliverables.
- Financial services, payments, fintech or regulated-environment experience is useful but not essential.
- Exposure to enterprise risk management and risk appetite frameworks is useful but not essential.
- Snowflake or another cloud data warehouse experience is useful but not essential.
- Familiarity with data pipelines, APIs, workflow orchestration, system integrations or GRC platforms is useful but not essential.
- Experience with statistical analysis, anomaly detection, forecasting, early-warning indicators, external intelligence, AI agents or LLMs is useful but not essential.
Benefits
Comp & perks- Equity participation: The opportunity to share in Teya's growth, subject to the applicable scheme and eligibility.
- Pension: Workplace pension provision.
- Holiday: Generous annual leave to support a healthy work-life balance.
- Flexible working: A flexible working environment built around collaboration, ownership and getting things done.
- Learning and development: Opportunities to develop your risk, data analytics and AI automation skills as the business grows.
- Health and wellbeing: Support for your wellbeing, with benefits designed to help you thrive at work and beyond.
- Team and company events: Opportunities to connect with colleagues, share ideas and celebrate success.