Qadeer Moinuddin Mohammed

• Employed automation processes using AWS tools like CloudFormation and Elastic Beanstalk, reducing infrastructure setup time, and improving team productivity by 25%.
Spearheaded a benchmark using Spark and Scala APIs to compare Spark against Hive and SQL, achieving a 30% performance improvement for data processing tasks.
• Utilized SQL queries for data transformation and enrichment, including aggregations, filtering, and integration of external datasets for enriching customer profiles.
• Designed an efficient data model using SQL for storing processed and enriched data, optimizing indexes for fast querying, and storage efficiency.
• Designed and executed Hive tables to store website log data in a structured format, enabling efficient analysis with Apache Spark, and reducing data processing time for marketing campaign performance analysis, leading to a 35% faster identification of key insights.
• Developed efficient ETL workflows using AWS Data Pipeline calculations for extraction, transformation, and loading, which reduced data processing time by 10%.
• Utilized Spark SQL to pre-process, cleanse, and join terabytes of data, achieving an improvement in data quality metrics, such as minimized errors by 20%.
Led a cross-functional team to implement automated dashboards and reporting using Power BI, empowering stakeholders with data-driven insights, and achieving a remarkable 25% improvement in workforce performance.

• Developed Python scripts for extracting data from web services API's and loading into databases.