About The RoleAbout 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.
What Success Looks Like — First 12 Months :
- * A prioritized BI and analytics roadmap tied to business decisions, with a consistent delivery cadence.
- A documented event taxonomy, business glossary, and KPI framework adopted across departments, with named owners.
- A semantic layer in production that you have directly helped build, powering dashboards and AI queries with tested, documented models.
- Live executive and departmental dashboards for UX, credit, collections, and investment, with weekly active use and clear evidence that they support decisions.
- Customer journey, funnel, and cohort models that connect product behavior to lending and operational outcomes.
- A golden-question evaluation suite with measured agent accuracy, agreed acceptance thresholds, and improvement quarter over quarter.
- Masking and access policies enforced on every dataset exposed to analysts or agents.
- Concrete examples of your analysis influencing business decisions, with outcomes tracked against agreed baselines.
RequirementsRequirements:
- 8+ years in business intelligence, analytics, analytics engineering, or related data roles, including ownership of cross-functional analytics priorities and company- or domain-level metric definitions.
- Recent, substantial hands-on experience writing SQL, building data models, delivering dashboards, and conducting analysis. You should be comfortable discussing work you personally built and how it changed a business decision.
- Expert SQL, including semi-structured data such as JSON at event and log scale, and strong Snowflake experience.
- Hands-on depth with dbt or an equivalent transformation or semantic layer, supported by strong data modeling, testing, and documentation practices.
- Strong BI development skills in Looker, Power BI, Tableau, or similar tools, and the judgment to know when a dashboard is the wrong answer.
- Practical product and event analytics experience, including sessionization, funnels, cohort analysis, and customer journey measurement.
- The ability to move between executive discussions and detailed implementation, translating ambiguous questions into reliable metrics, models, and recommendations.
- Strong business acumen and stakeholder fluency, including comfort with credit, collections, lending, or investment discussions and the willingness to challenge assumptions.
- Working familiarity with LLM and agent-based analytics, including text-to-SQL, semantic models for AI, and evaluation of model outputs.
- Experience setting technical standards and mentoring others while maintaining direct ownership of delivery.
- Practical understanding of data governance, access controls, and auditability in analytics environments.
Nice to Have:
- Fintech or lending experience, ideally in KSA/GCC or another regulated market.
- Experience building an analytics capability from an early foundation.
- Python for analysis and light automation.
- Experience implementing masking, row-level access, and data quality monitoring.
- Familiarity with OpenTelemetry or event instrumentation concepts.
- Arabic proficiency.
