Data Analyst - SC Cleared

The Senior Data Analyst supports the organization’s objective of enhancing supervisory effectiveness through better use of entity and regulatory data. The role helps transform how Supervision teams identify risks, priorities interventions and monitor firms, delivered through data-driven insights, dashboards, analytical tools and supervisory intelligence. It sits within a live testing programme for a regulatory data reporting regime. The analyst contributes to systems acceptance, firm feasibility and supervisory usability of the data being collected. The outcome is greater confidence in the regulatory data build through faster, more thorough testing., * Supporting Supervisory Outcomes: Analyse risk indicators, alerts and supervisory insights to enable risk-based supervision and decision making.

• Regulatory Data Analysis: Analyse authorisation, holdings, transactions, legal entity and regulatory reporting data. Create integrated views across firms and entities so supervisors see a coherent picture rather than fragmented sources.
• Supervisory Intelligence and Risk Analytics: Identify trends, anomalies, concentrations and emerging risks. Support thematic reviews, quantitative investigations and synthesis of alerts.
• Data Quality and Improvement: Assess data quality, implement validation controls and validate legal entity identifiers (LEIs). Enrich core datasets using external sources such as GLEIF, Companies House and market data proxies.
• Reporting and Decision Support: Develop Tableau dashboards, management information and self-service analytics for Supervision teams, moving towards reusable, automated reporting.
• Business Analysis and Stakeholder Engagement: Gather requirements, facilitate workshops and translate supervisory challenges into analytical solutions. Cohere disparate and conflicting business needs into workable plans through engagement and governance forums.
• Future State Capability: Support the move towards automated reporting, advanced analytics, continuous monitoring and early-warning indicators, building a mature supervisory intelligence capability.

• SQL and large-scale data analysis across complex, multi-source datasets.
• AWS services such as S3, Dynamo DB, Athena, etc.
• Data visualisation and dash boarding in Tableau or Power BI.
• Data quality assessment, validation and improvement.
• Stakeholder management across business and technical audiences.
• Regulatory or financial services experience