Role Description We are seeking a mid-level SQL & ETL Developer to support offshore data operations and ensure the reliability of critical data pipelines. This role will focus on monitoring, maintaining, and enhancing ETL processes while collaborating closely with U.S.-based teams. - Monitor and support daily ETL processes to ensure successful and timely execution - Troubleshoot and resolve issues related to ETL workflows, data pipelines, and job failures - Work with SSIS packages and assist in transitioning ETL logic into SQL stored procedures - Develop, maintain, and optimize SQL queries, stored procedures, and data transformations - Collaborate with onshore teams to support data availability aligned with business timelines - Assist in evolving ETL processes toward Python-based solutions (training/upskilling supported) - Support data transformations within the SQL layer for downstream reporting tools - Contribute to data modeling efforts, particularly for reporting and analytics use cases - Follow development best practices and work independently within established standards - Participate in Agile ceremonies including standups, sprint planning, and retrospectives Qualifications - 3-5 years of experience in SQL development and ETL support - Strong SQL skills, including query writing, performance tuning, and stored procedures - Experience with ETL tools, preferably SSIS - Ability to monitor and troubleshoot batch data processes and pipelines - Understanding of data transformation and data modeling concepts - Ability to work independently in a support-oriented environment Preferred Qualifications - Exposure to Python for data processing (or willingness to learn) - Experience with Tableau, including understanding of data logic and reporting layers - Familiarity with Power BI and data modeling concepts - Experience transitioning ETL logic from tools (e.g., SSIS) into SQL or code-based solutions - Prior experience working in an offshore/onshore support model Work Environment & Schedule - Offshore support role with close collaboration with U.S. teams - Must be available to support ETL processes aligned with U.S. Eastern Time (ET) - Typical collaboration hours begin around: 11:00 AM PST / 2:00 PM EST - Participation in Agile ceremonies (standups, sprint planning, retrospectives) held twice weekly

• Build and maintain CRMA dashboards, lenses, and datasets across the business, from exec KPI views to day-to-day operational reporting • Write SAQL queries, build dataflows and recipes, and keep datasets performing reliably, and recipes documented • Set up row-level security, sharing rules, and access controls across reporting environments • Keep dashboards fast and data fresh, tuning performance and refresh schedules as the org grows • Help us extend into Salesforce Data Cloud where it makes sense, connecting external data and working toward unified customer profiles • Work closely with architects, and business analysts to scope requirements and build the right solutions • Own problems when things break, tracing issues through data quality, pipeline logic, and dashboard behavior • Mentor junior team members, set standards for how we build in CRMA, and keep documentation in good shape

• 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.

Role 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. 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. Requirements - 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. 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.