Senior Data Analyst - Enterprise Risk
Role Overview
The Senior Data Analyst will help build Teya’s Enterprise Risk Management Framework and strengthen how risk information is consolidated, analysed and reported.
This is a business-facing analytical role working with the Head of Enterprise and Operational Risk and stakeholders across Credit, Fraud, AML, People, IT, Operations and Finance.
The role combines risk expertise, analytical problem-solving and practical AI capabilities.
Key Skills for This Role
Full Job Posting
About Teya
Teya builds a financial platform for local businesses across Europe, providing tools and support to help members run their businesses.
Role overview
The Senior Data Analyst will help build Teya’s Enterprise Risk Management Framework and strengthen how risk information is consolidated, analysed and reported.
This is a business-facing analytical role working with the Head of Enterprise and Operational Risk and stakeholders across Credit, Fraud, AML, People, IT, Operations and Finance.
The role combines risk expertise, analytical problem-solving and practical AI capabilities.
Responsibilities
- Map data sources and build repeatable datasets for KRIs across operational, credit, fraud, AML, people, capital, liquidity and other risk categories.
- Partner with risk stakeholders to understand priorities and turn reporting needs into analytical solutions.
- Analyse trends, assumptions, anomalies and changes in risk exposure using SQL and Python.
- Validate indicator definitions, assess data quality and analyse performance to inform KRI thresholds.
- Build dashboards and management reporting for monthly ERMF reporting and oversight.
- Automate reporting workflows using internal data and relevant external signals, with evidence, validation, traceability and human review.
- Design and test early-warning monitoring for unusual patterns and potential emerging risks.
- Specify data, integration and workflow needs for the GRC solution with Risk, Data and Technology colleagues.
- Document data sources, definitions, lineage, refresh frequencies, validation checks and limitations.
Tools and analytical approaches
- The description names Snowflake for datasets; SQL and Python for analysis; and Tableau, Power BI or similar tools for dashboards.
- AI-enabled workflows are intended to reduce manual reporting effort and surface emerging issues, with robust validation and human oversight.
Role qualifications
- The analyst should be able to understand risk and work effectively with risk owners, translating their requirements into practical data solutions.
- The description calls for use of SQL and Python, dashboard development, KRI and data-quality analysis, and careful documentation of data lineage and limitations.
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