Senior Data Analytics Specialist

PetroApp; United States; Remote; USD 100,000 - 140,000; Full time

Job summary

PetroApp is seeking a senior data analytics professional in the United States to translate strategic questions into actionable insights. You will own KPI design, governance, and self-service dashboards for executives, product, sales, finance, and operations, across multiple domains including fuel, transactions, and customer analytics.

The role emphasizes data storytelling, collaboration with product and engineering, and mentoring junior analysts.

Qualifications

• 5+ years of experience in data analytics, BI, product analytics, or related roles.
• Strong SQL and data manipulation across multiple domains.
• Experience building dashboards with BI tools and interpreting metrics.
• Ability to translate business questions into analytical plans and recommendations.
• Experience with transactions, product, or financial datasets at scale.
• Proficiency in Python or R for analysis and automation.
• Excellent communication to non-technical stakeholders.
• Commitment to data accuracy and reproducibility.

Responsibilities

• Translate strategic questions into analyses, dashboards, and reports for leadership.
• Own KPI definitions, governance, and metrics across business domains.
• Build reliable self-service dashboards for executives and teams.
• Analyze user journeys, adoption, cohorts, and product metrics to guide growth.
• Support reconciliation, revenue tracking, and cost analyses.
• Identify anomalies and fraud patterns to enhance trust and controls.
• Design experiments and forecasts for pilots and campaigns.
• Tell data stories with actionable recommendations.
• Collaborate with data engineering to improve data quality and datasets.
• Mentor junior analysts on standards and best practices.

Skills

Advanced SQL skills

BI dashboard design

KPI design & governance

Python or R for analysis

Data storytelling & communication

Stakeholder management

Data quality & documentation

Experimentation & forecasting

Tools

Power BI

Tableau

Looker

Metabase

Superset

dbt

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.

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

• 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.

• 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.