Job description
Business insight and decision support: Translate strategic and operational questions into clear analyses, dashboards, reports, and recommendations for leadership and functional teams.
KPI and metrics ownership: Define, document, and govern business metrics across fuel consumption, transaction activity, customer adoption, fleet performance, station coverage, invoicing, product usage, savings, churn, and operational efficiency.
Dashboard and reporting delivery: Build reliable self-service dashboards for executives, product, sales, finance, operations, customer success, and country or regional teams.
Customer and product analytics: Analyze user journeys, feature adoption, customer cohorts, fleet behavior, transaction trends, fuel limits, budget usage, and drop-off points to guide product and growth decisions.
Operations and finance analytics: Support reconciliation, invoicing, station performance, wallet movement, service usage, cost analysis, revenue tracking, and profitability insights.
Fraud and anomaly insight: Partner with product, operations, and data engineering to identify unusual fuel patterns, tampering indicators, policy exceptions, and monitoring rules that improve trust and control.
Experimentation and forecasting: Design analyses for pilots, pricing, campaigns, product launches, and operational changes; support forecasting for consumption, transactions, customer demand, and station utilization.
Data storytelling: Present insights clearly, explain trade-offs, quantify impact, and convert analysis into practical recommendations and action plans.
Data quality partnership: Work with data engineering to improve source data, metric definitions, documentation, dashboard reliability, and analytics-ready datasets.
Analytics mentorship: Set standards for analysis quality, dashboard design, metric governance, and stakeholder communication while mentoring less experienced analysts.
Competitive salary and benefits package.
Opportunity to work on cutting-edge technology with a passionate team.
Career growth and development opportunities.
A collaborative and inclusive work environment.
Required qualifications 5+ years of experience in data analytics, business intelligence, product analytics, revenue analytics, operations analytics, or a similar analytical role.
Advanced SQL skills with the ability to independently extract, transform, join, validate, and analyze complex data from multiple domains.
Strong experience building dashboards and data products using Power BI, Tableau, Looker, Metabase, Superset, or similar BI tools.
Strong understanding of KPI design, metric definitions, funnel analysis, cohort analysis, segmentation, trend analysis, forecasting, and root-cause analysis.
Ability to convert ambiguous business questions into analytical plans, structured hypotheses, and actionable recommendations.
Experience working with transactional, product, customer, payment, operational, or financial datasets at scale.
Working knowledge of Python or R for analysis, automation, statistical exploration, or notebook-based research.
Excellent stakeholder management and communication skills, including the ability to explain technical findings to non-technical audiences.
Strong attention to data accuracy, definitions, documentation, and reproducibility.
Comfort working in fast-paced product and engineering environments with changing priorities and high ownership expectations.
Preferred qualifications Experience in fintech, fleet management, logistics, mobility, fuel, marketplace, SaaS, or high-volume transaction businesses.
Experience with dbt, semantic layers, data catalogs, metric stores, or analytics engineering workflows.
Familiarity with fraud analytics, anomaly detection, operational controls, pricing analysis, or customer savings measurement.
Experience with A/B testing, causal inference, retention analysis, churn prediction, LTV modeling, or commercial performance analytics.
Arabic and English business communication skills are a plus for regional stakeholder engagement.
Core analytics stack expectations The exact stack may evolve, but the successful candidate should be comfortable operating across the following categories: Analysis: SQL, spreadsheets, Python or R, notebooks, statistical methods, and business case modeling.
BI and visualization: Power BI, Tableau, Looker, Metabase, Superset, or equivalent dashboarding tools.
Data modeling: dimensional thinking, metric definitions, cohort tables, funnel tables, and curated analytical datasets.
Collaboration: requirements gathering, stakeholder workshops, documentation, presentations, and decision memos.
Governance: metric catalog, dashboard ownership, access control awareness, and data quality issue management.
Product analytics: event data, customer journeys, feature usage, adoption metrics, retention, and conversion analysis.
