Data Analyst

• 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, DynamoDB, Athena, etc.
• Data visualisation and dashboarding in Tableau or Power BI.
• Data quality assessment, validation and improvement.
• Stakeholder management across business and technical audiences.
• Regulatory or financial services experience.

Desirable Skills:

• Familiarity with regulatory or financial data reporting regimes.

Must Have:

• Highly proactive and self-driven - sets direction and momentum without close supervision.
• Strategically astute - operates across different business areas, building knowledge and relationships rapidly.
• Adaptable - pivots to address new challenges at short notice as contexts change.
• Data-fluent - experience driving data-focused projects with a solid grasp of common data challenges in large organisations.
• Coheres competing needs - reconciles disparate business requirements into deliverable plans via engagement, workshops and governance.