T Ahmed
Data Engineer @Wells Fargo
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WORK HISTORY
Data Engineer @Wells Fargo
NJ, US
Utilized Python, SQL & Spark to design and implement data extraction and transformation processes, automation, data optimization, streamlining workflows, and delivering actionable insights that improved data accessibility and reduced analysis time by 30%.Leveraged AWS services, including SQS, SNS, Lambda, Redshift, and Glue, to design scalable data pipelines and optimize cloud storage, resulting in a 40% improvement in data processing efficiency & used Airflow for orchestration.Utilized Jenkins, and GitHub to automate continuous integration and continuous deployment (CI/CD) processes, significantly reducing deployment times by 50% and improving the overall software delivery pipeline\'s efficiency.Proficient in MySQL, utilizing advanced querying techniques, data modeling, and optimization strategies to manage and analyze relational databases, ensuring data integrity and efficient retrieval for informed decision-making.Designed and implemented optimized Snowflake data warehouses, leveraging features like clustering, data partitioning, and time travel to improve query performance and reduce costs.Developed and optimized ETL pipelines on Databricks, processing terabytes of data using Apache Spark and Delta Lake.Led the migration of on-premise data processing tasks to Databricks on AWS, reducing processing time by 40%.Developed interactive dashboards and data visualizations in Tableau, enabling stakeholders to gain actionable insights from complex datasets and improving data-driven decision-making by 30% across the organization.Skilled in MongoDB and NoSQL databases, employing schema-less data models to efficiently store, query, and retrieve large volumes of unstructured data, ensuring high performance and scalability for modern applications.
EDUCATION
University of New Brunswick
Bachelor's of Computer Science
ABOUT T AHMED
Results-driven Data Engineer with 6+ years of experience designing, building, and optimizing scalable data pipelines and architectures. Expert in leveraging big data technologies such as Hadoop, Spark, and Kafka to process massive datasets, and skilled in SQL, Python, and cloud platforms like AWS and Azure to ensure efficient data management and analysis.
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