Results-driven Data Analyst and BI Analyst with 5 years of experience delivering actionable insights and automated analytics solutions for product, marketing, and business teams. Specialized in SQL, Power BI, Tableau, an…
Results-driven Data Analyst and BI Analyst with 5 years of experience delivering actionable insights and automated analytics solutions for product, marketing, and business teams. Specialized in SQL, Power BI, Tableau, and Python, I design and deploy dashboards, streamline ETL pipelines, and lead data-driven projects that improve operational efficiency and strategic decision-making.
Proven success in reducing reporting time by 40%, boosting decision speed by 25%, and enhancing customer retention through targeted analytics. Experienced in retail, e-commerce, SaaS, and service sectors, with a Master's in Artificial Intelligence from Queen Mary University of London. Seeking to leverage my expertise in data analysis, growth experiments, and business intelligence to drive measurable impact for organizations in Dubai and the wider UAE/GCC region.
Led end-to-end analytics delivery for 50+ global clients across retail, e-commerce, and SaaS, producing executive level KPI frameworks and decision-ready insights for C-suite and functional leaders.
Engineered automated ETL pipelines using Python, SQL, and Apache Airflow, reducing reporting latency by 40% and improving data reliability across batch workflows.
Designed and deployed 20+ interactive dashboards in Power BI and Tableau covering revenue, CAC, retention, conversion funnels, and profitability; improved decision turnaround time by over 27%.
Conducted advanced customer segmentation using Python (Pandas, NumPy, scikit-learn) and SQL, improving campaign targeting, precision and driving a 20% increase in repeat engagement.
Provided technical mentorship to a team of seven analysts in BI development, data modelling, and exploratory analysis, resulting in a 30% increase in team output and reporting consistency.
Led end-to-end analytics delivery for 50+ global clients across retail, e-commerce, and SaaS, producing executive level KPI frameworks and decision-ready insights for C-suite and functional leaders. Engineered automated ETL pipelines using Python, SQL, and Apache Airflow, reducing reporting latency by 40% and improving data reliability across batch workflows. Designed and deployed 20+ interactive dashboards in Power BI and Tableau covering revenue, CAC, retention, conversion funnels, and profitability; improved decision turnaround time by over 27%. Conducted advanced customer segmentation using Python (Pandas, NumPy, scikit-learn) and SQL, improving campaign targeting, precision and driving a 20% increase in repeat engagement. Provided technical mentorship to a team of seven analysts in BI development, data modelling, and exploratory analysis, resulting in a 30% increase in team output and reporting consistency.
Designed and deployed Power BI dashboards for finance and operations, covering revenue, CLV, CAC, retention, and profitability KPIs; reduced reporting cycles by 40% and improved strategic decision making by 30%.
Performed in-depth analysis of Shopify transactional, behavioural, and marketing data using SQL and Python, contributing to a 15% increase in campaign efficiency and clearer ROI tracking.
Built a multi-layer ROI attribution framework incorporating CLV, margin structures, and channel performance, improving marketing budget allocation accuracy by 25%.
Developed role-specific, drill-down dashboards with real-time filtering to strengthen self-service analytics and reduce dependency on manual reports.
Collaborated with product and marketing leads to define KPI structures, validate data pipelines, and improve data integrity, resulting in a 20% reduction in reporting discrepancies.
Designed and deployed Power BI dashboards for finance and operations, covering revenue, CLV, CAC, retention, and profitability KPIs; reduced reporting cycles by 40% and improved strategic decision making by 30%. Performed in-depth analysis of Shopify transactional, behavioural, and marketing data using SQL and Python, contributing to a 15% increase in campaign efficiency and clearer ROI tracking. Built a multi-layer ROI attribution framework incorporating CLV, margin structures, and channel performance, improving marketing budget allocation accuracy by 25%. Developed role-specific, drill-down dashboards with real-time filtering to strengthen self-service analytics and reduce dependency on manual reports. Collaborated with product and marketing leads to define KPI structures, validate data pipelines, and improve data integrity, resulting in a 20% reduction in reporting discrepancies.
Designed and implemented analytical data models and Power BI dashboards for 15+ retail and e-commerce clients, reducing reporting turnaround by 35% and improving leadership decision latency.
Conducted customer trend and behavioural analysis using SQL and Python, enabling targeted retention campaigns that increased repeat engagement by 20%.
Developed and deployed machine learning models for churn prediction and performance optimisation, contributing to an 18% reduction in customer attrition through personalised interventions.
Built recommendation systems using Python, SQL, and collaborative filtering techniques, improving recommendation accuracy by 20% and strengthening user retention.
Automated end-to-end data pipelines, improving data flow reliability and reducing manual workload by 30%, enabling the analytics team to shift focus toward strategic insights.
Designed and implemented analytical data models and Power BI dashboards for 15+ retail and e-commerce clients, reducing reporting turnaround by 35% and improving leadership decision latency. Conducted customer trend and behavioural analysis using SQL and Python, enabling targeted retention campaigns that increased repeat engagement by 20%. Developed and deployed machine learning models for churn prediction and performance optimisation, contributing to an 18% reduction in customer attrition through personalised interventions. Built recommendation systems using Python, SQL, and collaborative filtering techniques, improving recommendation accuracy by 20% and strengthening user retention. Automated end-to-end data pipelines, improving data flow reliability and reducing manual workload by 30%, enabling the analytics team to shift focus toward strategic insights.
Enhanced an existing industry recommendation model by developing a hybrid system using Python, Pandas, and scikit-learn with real e-commerce data, employing data preprocessing, feature engineering, and ML algorithms (matrix factorisation, item-based filtering). Achieved a 20% improvement in recommendation accuracy, significantly boosting user engagement on a live platform. Skills: Data Wrangling, Python, ML Modelling, A/B Testing, Business Impact
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