Ahamed Abdullah Mohammed

• Designed and implemented end-to-end data pipelines on Azure Data Factory and Azure Databricks for efficient data ingestion and transformation.
• Developed analytics-ready schemas using dimensional modeling to enhance reporting performance.
• Established automated monitoring and data quality frameworks to ensure dataset accuracy and compliance.
• Optimized Spark and SQL jobs, achieving 30% reduction in processing time and improved data availability.
• Automated pipeline deployments with Azure DevOps CI/CD, increasing deployment efficiency by 25%.
• Maintained incremental and partitioned ETL/ELT pipelines for cost-effective data storage and quicker querying.
• Collaborated with BI teams to deliver insights through Power BI dashboards and reports.

Environment: Azure Data Factory, Azure Databricks, Azure Data Lake, Azure Synapse Analytics, Azure SQL Database, Spark, PySpark, Python, SQL, Power BI, Azure DevOps, ETL/ELT pipelines

• Designed and implemented scalable data pipelines on Google Cloud Platform (GCP) using Dataflow, Apache Beam, and Pub/Sub for real-time and batch processing.
• Processed large datasets with Google Cloud Dataproc and Apache Spark to facilitate high-performance analytics.
• Integrated machine learning models via Google AI Platform and TensorFlow for predictive analytics in insurance workflows.
• Developed real-time streaming solutions with Pub/Sub and Dataflow for event-driven applications.
• Automated deployment and testing of data pipelines using Cloud Build, Terraform, and CI/CD practices.
• Optimized data processing workflows with physics-based modeling, achieving a 95% reduction in processing time over six months.
• Analyzed telemetry data with JavaScript to identify trends, enhancing system performance by 30% within six months.

Environment: Google Cloud Platform (GCP), Dataflow, Dataproc, Pub/Sub, BigQuery, TensorFlow, Apache Beam, Apache Spark, Python, SQL, Google AI Platform, Cloud Build, Terraform, JavaScript

• Designed and implemented scalable data pipelines using Apache Spark, Hadoop, and ETL tools for financial data management.
• Optimized data workflows with SQL Server and PostgreSQL, achieving 30% improvement in processing efficiency.
• Developed real-time streaming solutions utilizing Apache Kafka for high-throughput transaction processing.
• Implemented data governance frameworks to enhance quality, reducing errors by 30% and improving reporting efficiency.
• Built large-scale ETL pipelines for complex datasets, boosting processing efficiency by 30%.
• Applied machine learning techniques to improve predictive accuracy by 30%, supporting informed decision-making.
• Created advanced visualizations using Python and Tableau to present findings and foster collaboration.

Environment: Apache Spark, Hadoop, ETL tools, SQL Server, PostgreSQL, Apache Kafka, Python, Tableau, Machine Learning (Python/Scikit-learn)