Muhammad Ali

Data Analyst

Oracle • Meio período

Oct 2025 - Present • 8 mos

• Delivered Power BI dashboards that cut report generation time from 1 day to under 15 minutes, increasing executive decision turnaround by 60% and enabling real-time business insights for C-suite leadership • Led cross-team implementation of Delta Lake in Azure Databricks, improving data accuracy by 25% through ACID transactions and reducing storage costs by 15% via optimized file compression and partitioning strategies • Successfully transformed Power BI dashboards to Sigma Computing platform, achieving 40% cost savings through reduced Premium capacity requirements while maintaining full analytical capabilities and improving query performance • Implemented end-to-end Databricks solutions using Apache Spark for large-scale data processing, handling 5M+ records daily and reducing ETL pipeline execution time by 50% through parallel processing and cluster optimization • Developed comprehensive technical runbooks, ETL documentation, and BI usage guides covering 20+ data pipelines and dashboards, reducing onboarding time for new team members by 40% and improving knowledge transfer • Designed and deployed scalable data lakehouse architecture on Azure Databricks integrating Delta Lake tables with medallion architecture (Bronze, Silver, Gold layers) ensuring data quality and governance • Configured automated job scheduling and monitoring for Databricks workflows using Azure Data Factory, implementing error handling and alerting mechanisms that improved pipeline reliability to 99.5% • Collaborated with data science and analytics teams to build reusable data engineering frameworks and best practices, establishing coding standards and version control workflows using Git and Azure DevOps

Business Intelligence Engineer

Microsoft • Período integral

Jul 2022 - Present • 3 yrs 11 mos

Built SSIS-based ETL workflows integrating Salesforce, NetSuite, and AccessPay, eliminating 60% of manual reconciliation work and improving data delivery timeliness from 48 hours to 4 hours through automated data pipelines • Developed OLAP cubes using SQL Server Analysis Services (SSAS) and created 25+ SSRS reports that improved sales forecasting accuracy by 20%, enabling better inventory management and pricing strategies for business stakeholders • Optimized T-SQL queries and stored procedures through execution plan analysis and indexing strategies, reducing average query execution time by 45% and lowering SQL Server resource usage by 25% • Created comprehensive data model diagrams, API integration references, and SQL optimization playbooks documenting best practices for 15+ systems, cutting developer troubleshooting time by 30% and improving team efficiency • Delivered interactive Power BI dashboards with drill-down capabilities and real-time data refresh that increased user adoption by 50% across departments and improved decision-making efficiency as measured by 35% reduction in ad-hoc report requests • Designed and implemented dimensional data models including Star and Snowflake schemas with 10+ fact tables and 20+ dimension tables, implementing Slowly Changing Dimensions (SCD Types 1, 2, 3) for historical tracking • Managed end-to-end data warehouse lifecycle from requirements gathering to deployment, integrating data from multiple sources (ERP, CRM, databases, APIs) serving 80+ business users • Configured SQL Server Agent jobs and alerts for automated ETL workflows, ensuring 99.5% job success rate and implementing comprehensive error handling and notification systems • Collaborated with business stakeholders to translate requirements into technical specifications, delivering BI solutions with 95% first-time acceptance rate through iterative development and user feedback

Data Engineer

Google • Freelance

Jan 2021 - Dec 2023 • 2 yrs 11 mos

• Delivered Power BI dashboards that cut report generation time from 1 day to under 15 minutes, increasing executive decision turnaround by 60% and enabling real-time business insights for C-suite leadership • Led cross-team implementation of Delta Lake in Azure Databricks, improving data accuracy by 25% through ACID transactions and reducing storage costs by 15% via optimized file compression and partitioning strategies • Successfully transformed Power BI dashboards to Sigma Computing platform, achieving 40% cost savings through reduced Premium capacity requirements while maintaining full analytical capabilities and improving query performance • Implemented end-to-end Databricks solutions using Apache Spark for large-scale data processing, handling 5M+ records daily and reducing ETL pipeline execution time by 50% through parallel processing and cluster optimization • Developed comprehensive technical runbooks, ETL documentation, and BI usage guides covering 20+ data pipelines and dashboards, reducing onboarding time for new team members by 40% and improving knowledge transfer • Designed and deployed scalable data lakehouse architecture on Azure Databricks integrating Delta Lake tables with medallion architecture (Bronze, Silver, Gold layers) ensuring data quality and governance • Configured automated job scheduling and monitoring for Databricks workflows using Azure Data Factory, implementing error handling and alerting mechanisms that improved pipeline reliability to 99.5% • Collaborated with data science and analytics teams to build reusable data engineering frameworks and best practices, establishing coding standards and version control workflows using Git and Azure DevOps