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Teya is a Series C fintech building a financial platform for local businesses across Europe. The company is establishing its Enterprise Risk Management Framework (ERMF) and seeking a Senior Data Analyst to turn risk data into reliable measures, meaningful insights and actionable reporting.
In this business-facing analytical role, you will work closely with the Head of Enterprise & Operational Risk and stakeholders across Credit, Fraud, AML, People, IT, Operations and Finance. Your responsibilities include:
- Developing risk data and reporting by identifying and mapping relevant data sources using Snowflake to build repeatable datasets for Key Risk Indicators (KRIs) across operational, credit, fraud, AML, people, capital, liquidity and other risk categories.
- Partnering with risk stakeholders to understand priorities, clarify reporting requirements and translate risk management needs into practical analytical solutions.
- Analysing and monitoring risk indicators using SQL and Python to investigate trends, test assumptions, identify anomalies and understand changes in risk exposure.
- Supporting KRI definitions and thresholds by validating indicator definitions, assessing data quality and analysing historical performance to inform threshold calibration.
- Building dashboards and management reporting using Tableau, Power BI or similar tools to support monthly ERMF reporting and management oversight.
- Automating risk reporting with AI by building workflows that bring together internal data and external signals (regulatory updates, market developments, news, emerging threats) to generate evidence-based reporting drafts.
- Developing early-warning capabilities to monitor KRI movements and operational data, highlighting unusual patterns and potential emerging risks.
- Supporting risk technology and integration by translating ERMF requirements into practical data, integration and workflow specifications for Teya's GRC solution.
- Improving data quality and governance by documenting data sources, definitions, lineage, refresh frequencies, validation checks and limitations.
- Driving continuous improvement by identifying opportunities to simplify reporting and strengthen how risk information is used across the business.
Success means trusted risk data, meaningful risk insight that helps stakeholders make informed decisions, effective business partnering, timely and evidence-based management reporting, earlier identification of emerging risks, reliable AI-enabled workflows, and stronger risk data foundations.
REQUIREMENTS:
- 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 with ability to work closely with risk owners and non-technical stakeholders, translating needs into meaningful analysis and reporting.
- Working knowledge of SQL and Python for data analysis and practical automation (comfort using these tools with AI assistance and validating results).
- Experience building dashboards and management reports using Tableau, Power BI or similar visualisation tools.
- Strong analytical judgement with ability to challenge assumptions, investigate unexpected results, assess data quality and communicate clearly what data does and does not show.
- Experience working with data from multiple sources and improving reliability, consistency and usability of analytical outputs.
- Hands-on curiosity about current AI models and AI-enabled workflow automation, with ability to build and improve workflows supporting data gathering, analysis and risk reporting.
- Structured, pragmatic approach to problem-solving with 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 with Snowflake or another cloud data warehouse.
- Familiarity with data pipelines, APIs, workflow orchestration or system integrations.
- Exposure to 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, LLMs or AI-enabled workflow automation tools.
