Senior Data Analytics Specialist
PetroApp · Remote
- 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.
Requirements
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.
Benefits
- 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.