Analytical and detail-oriented Data Analyst with hands-on experience in SQL, Python, Advanced Excel, Power BI, Tableau, and DOMO, specializing in converting large and complex datasets into clear, actionable business insi…
Analytical and detail-oriented Data Analyst with hands-on experience in SQL, Python, Advanced Excel, Power BI, Tableau, and DOMO, specializing in converting large and complex datasets into clear, actionable business insights. Experienced in social listening analytics (Sprinklr), text analytics, sentiment analysis, KPI tracking, and dashboard development across retail, e-commerce, and marketing domains.
Proven ability to build and optimize SQL pipelines, automate reporting workflows, enhance data quality, and deliver insights that support business decisions. Previously worked with Target Corporation on large-scale retail datasets (100M+ rows) and currently supporting brand intelligence and social listening analytics for Microsoft.
A fast learner with strong problem-solving skills, stakeholder communication, and the ability to thrive in fast-paced environments. Actively seeking Data Analyst / BI Analyst roles in Dubai to contribute data-driven decision-making and end-to-end reporting excellence.
Led end-to-end social listening & text analytics for Microsoft’s SIP team using Sprinklr across Twitter, Reddit, blogs, news, and tech communities focused on AI, Cloud, and ML. Engineered advanced Sprinklr queries (Boolean, conceptual, branded/unbranded), boosting data precision and reducing noise by 30%; supported query audits and sampling validation. Converted large-scale unstructured data into actionable intelligence through categorization, sentiment analysis, entity tagging, and thematic clustering for brand, product, and competitive insights. Proactively monitored daily brand health signals—dissatisfaction drivers, feature requests, misinformation spikes, anomalies, and competitive threats—enabling timely stakeholder decisions. Standardized listening metrics (Volume, Sentiment, Engagement, Reach, SOV, Topic Clusters) and built executive-ready dashboards & reports showcasing brand health and competitive benchmarks. Ensured strong data integrity by validating sources, removing duplicates, running logic checks, and aligning to Microsoft’s global reporting standards. Leveraged AI analytics + Excel + Python/SQL to automate preprocessing and reporting, reducing manual effort by 25% and elevating insight quality.
Built a Decision Tree classifier using the Bank Marketing dataset, achieving 85% accuracy in predicting customer purchases. Analyzed traffic accident data, identifying patterns related to road conditions, weather, and time, leading to a 12% improvement in predictive analysis. Conducted data cleaning and exploratory data analysis (EDA), creating more than 10+ visualizations (bar charts, histograms, etc.) to uncover meaningful trends and actionable insights.
Built and optimized enterprise-scale SQL pipelines in Trino & Hive (Tez), processing 100M+ row retail, traffic, planning, and marketplace datasets to support strategic dashboards and weekly business reviews. Engineered and reconciled SAT metric suites (Sales, Demand, AOV, Fill Rate, Traffic, Net Orders, Ad Revenue, Purchasability) across 40-level merchandising hierarchies, improving reporting accuracy by ~98% for S&P, C&E, Target+, and executive reporting. Designed and automated Plan and PQF metric layers, updated activation logic, and reduced manual QA effort by 60%, improving pipeline reliability and refresh stability. Performed root-cause analysis on data quality issues (duplication, aggregation drift, missing hierarchies) across TY/LW/LY/YTD datasets with end-to-end metric validation, reducing recurring issues by 40%. Enhanced DOMO dashboards (Performance Hub, Executive Summary, SAT) by integrating new KPIs, adding dynamic filters, and aligning SQL backend with frontend visual behavior, improving data correctness and stakeholder usability by ~35%. Delivered deep-dive merchandising analytics (SKU performance, assortment, seasonality, vendor matrices), enabling category teams to identify growth opportunities and operational issues. Conducted Ratings & Reviews analytics across Target, Amazon, and Walmart to assess vendor sentiment, product-level issues, and customer experience improvement themes. Applied advanced SQL optimization techniques (window function restructuring, join elimination, CTE refactoring) to improve run times and reduce compute costs, enhancing query performance by 30–50%. Led systematic metric mismatch debugging, performing field-level tracebacks, hierarchy audits, and cross-source validations to ensure KPI consistency. Developed strong domain expertise in retail analytics, clickstream behavior, inventory planning, marketplace operations, and hierarchical KPI governance.
Applied PyTorch Geometric for node classification, link prediction, and anomaly detection, improving model accuracy by 18%.
Leveraged BioBERT for biomedical entity recognition and relation extraction, increasing precision in text-based biomedical tasks by 15%.
Developed a multilingual medical dataset by translating over 200 medical records into Tamil using NotoSansTamil font.
Designed and integrated patient and doctor applications, reducing data retrieval time by 30% and streamlining healthcare management.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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