Director of Business Intelligence & Analytics

About the role

You will be Soar’s hands‑on leader for business intelligence and analytics, responsible for both strategic direction and the delivery of actionable insights. Working closely with leaders across UX, credit, collections, investment and operations, you will translate business questions into SQL queries, semantic models, dashboards and AI‑driven answers.

Key responsibilities

• Set priorities, own the BI & analytics roadmap and deliver critical projects from question to adoption.
• Design, implement and maintain the event taxonomy, entity models and metric definitions on Snowflake using dbt.
• Write SQL, test hypotheses and turn findings into clear recommendations for multiple departments.
• Develop and maintain executive and departmental dashboards, measuring adoption and retiring low‑value reports.
• Define KPIs with department leaders, resolve conflicting definitions and maintain a business glossary.
• Curate semantic models for AI agents, build a golden‑question evaluation set and investigate accuracy issues.
• Build sessionisation, funnel and cohort models linking product behavior to lending outcomes.
• Partner with security, GRC and architecture to enforce PII masking, row‑level access and auditability.
• Identify data quality gaps, define business requirements and work with architecture on data contracts.
• Establish testing, documentation and version‑control practices; hire and mentor analysts as needed.

Required profile

• 8+ years in business intelligence, analytics engineering or related data roles with cross‑functional ownership.
• Recent, substantial hands‑on experience writing SQL, building data models and delivering dashboards.
• Strong business acumen and ability to engage with credit, collections, lending or investment stakeholders.
• Experience setting technical standards while remaining an active contributor.

Required skills

• Expert SQL, including JSON handling at event scale.
• Snowflake data warehouse.
• dbt (or equivalent transformation/semantic layer) with testing and documentation.
• BI tools: Looker, Power BI, Tableau (or similar).
• Product/event analytics: sessionisation, funnels, cohort analysis.
• Familiarity with LLM‑based analytics, text‑to‑SQL and semantic models for AI agents.