锦鲤求职岗位描述
Design and implement semantic models for key domains, including customer/entity, onboarding journeys, credit exposures, limits, collateral, risk ratings, portfolio hierarchies, and performance measures. Define and govern KPIs/metrics (e.g., onboarding turnaround time, KYC SLA adherence, application drop-off, approval rate, exposure at default, utilisation, arrears/delinquency, portfolio concentration, risk-adjusted returns), with documentation and lineage. Translate business requirements into standard measures and conformed dimensions to ensure cross-domain consistency (e.g., customer, product, legal entity, booking location, time, risk grade). Implement data quality and reconciliation controls to ensure metric correctness against source-of-truth platforms. Optimise semantic performance and cost through appropriate modelling patterns, aggregation strategies, and incremental refresh approaches. Apply security controls (RBAC/ABAC), row/column-level security and auditing to support controlled access to sensitive onboarding and credit data. Operate the semantic layer in production: CI/CD, monitoring, incident triage, root cause analysis and continuous improvement. Partner with Data Engineering, Product Owners, Analysts, Risk and Operations stakeholders to drive adoption and consistent usage. Establishing “one agreed definition” for critical onboarding, credit and portfolio KPIs used across squads. Reducing time spent reconciling numbers across reports, dashboards and AI outputs. Enabling scalable self-serve analytics with clear, discoverable, versioned metric definitions. Maintaining a stable, performant semantic layer with strong controls and auditability. Experience designing semantic layers for regulated domains (KYC/AML, credit risk, portfolio analytics).