About The Role

About The Role

You will be Soar’s hands-on leader for business intelligence and analytics, accountable for both the direction of the function and the work that makes it useful. You will work directly with business leaders to identify the right questions, write SQL, build semantic models, develop dashboards, and turn analysis into decisions across UX, credit, collections, investment, and operations.

You will own the layer between raw data and business answers: the event taxonomy, metric definitions, and curated semantic models that both people and AI agents rely on. Your ownership includes setting priorities, establishing standards, and personally delivering the foundations and most consequential analytical work.

The boundaries are explicit. You own the semantic layer, business definitions, and analytics products. The architecture team owns ingestion pipelines and data contracts. The AI team owns agent tooling and orchestration. You supply the trusted business foundation they build on.

This is a director-level individual contributor and player-coach role, with a substantial share of your time spent building and analyzing. As the function grows, you will hire and mentor selectively while remaining directly involved in delivery. Success means departments make better decisions faster and AI agents provide answers the business can trust.

Key Responsibilities

• Set priorities and deliver the work. Own the BI and analytics roadmap, agree priorities with business leaders, and personally take critical projects from business question through modeling, analysis, and adoption.
• Build and own the semantic layer. Design, implement, and maintain the event taxonomy, entity models covering users, devices, sessions, and loans, and business metric definitions on Snowflake using dbt or an equivalent transformation layer.
• Deliver decision-ready analysis. Write SQL, investigate performance changes, test business hypotheses, and translate findings into clear recommendations for UX, credit, collections, investment, and operations.
• Build BI products people use. Develop and maintain executive reporting and departmental dashboards, with named business owners and defined decisions behind each one. Measure adoption and improve or retire reporting based on its value.
• Establish consistent business metrics. Work directly with department leaders to define KPIs, resolve conflicting definitions, and maintain a practical business glossary that supports reporting and self-service analysis.
• Make AI analytics trustworthy. Curate the semantic models agents query, build and maintain a golden-question evaluation set using real business questions and verified answers, review agent-generated SQL, and investigate accuracy failures with the AI team.
• Reconstruct the customer journey. Build sessionization, funnel, and cohort models spanning mobile interaction → API → decision → outcome, using trace and correlation IDs to connect events across systems.
• Implement governance in the analytics layer. Partner with security, GRC, and architecture to implement and verify PII masking, row-access policies, department-scoped access, and auditability across analyst- and agent-accessible datasets.
• Improve data at the source. Identify instrumentation gaps and data quality issues through hands-on investigation. Define business requirements and work with architecture on producer-level data contracts and fixes.
• Raise the quality of analytics delivery. Establish testing, documentation, version control, and review practices. Hire and mentor analysts or analytics engineers as needed, remaining an active contributor to models, code, and analysis.